2 + 3 # 2 더하기 3
## [1] 5
(3 + 6) * 8
## [1] 72
2 ^ 3 # 2의 세제곱
## [1] 8
8 %% 3
## [1] 2R Basic
Basic
Code
R
R 기초
모두를 위한 R 데이터 분석 입문
2장. 변수와 벡터
7 + 4
## [1] 11log(10) + 5 # 로그함수
## [1] 7.302585
sqrt(25) # 제곱근
## [1] 5
max(5, 3, 2) # 가장 큰 값
## [1] 5a <- 10
b <- 20
c <- a+b
print(c)
## [1] 30a <- 125
a
## [1] 125
print(a)
## [1] 125a <- 10 # a에 숫자 저장
b <- 20
a + b # a+b의 결과 출력
## [1] 30
a <- "A" # a에 문자 저장
a + b # a+b의 결과 출력. 에러 발생
## Error in a + b: non-numeric argument to binary operatorx <- c(1, 2, 3) # 숫자형 벡터
y <- c("a", "b", "c") # 문자형 벡터
z <- c(TRUE, TRUE, FALSE, TRUE) # 논리형 벡터
x ; y ;z
## [1] 1 2 3
## [1] "a" "b" "c"
## [1] TRUE TRUE FALSE TRUEw <- c(1, 2, 3, "a", "b", "c")
w
## [1] "1" "2" "3" "a" "b" "c"v1 <- 50:90
v1
## [1] 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74
## [26] 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90
v2 <- c(1, 2, 5, 50:90)
v2
## [1] 1 2 5 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71
## [26] 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90v3 <- seq(1, 101, 3)
v3
## [1] 1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55
## [20] 58 61 64 67 70 73 76 79 82 85 88 91 94 97 100
v4 <- seq(0.1, 1.0, 0.1)
v4
## [1] 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0v5 <- rep(1, times = 5) # 1을 5번 반복
v5
## [1] 1 1 1 1 1
v6 <- rep(1:5, times = 3) # 1에서 5까지 3번 반복
v6
## [1] 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5
v7 <- rep(c(1, 5, 9), times = 3) # 1, 5, 9를 3번 반복
v7
## [1] 1 5 9 1 5 9 1 5 9
v8 <- rep(1:5, each = 3) # 1에서 5를 각각 3번 반복
v8
## [1] 1 1 1 2 2 2 3 3 3 4 4 4 5 5 5
rep(1:3, each = 3, times = 3)
## [1] 1 1 1 2 2 2 3 3 3 1 1 1 2 2 2 3 3 3 1 1 1 2 2 2 3 3 3
rep(1:3, times = 3, each = 3)
## [1] 1 1 1 2 2 2 3 3 3 1 1 1 2 2 2 3 3 3 1 1 1 2 2 2 3 3 3score <- c(90, 85, 70) # 성적
score
## [1] 90 85 70
names(score) # score에 저장된 값들의 이름을 보이시오
## NULL
names(score) <- c("John", "Tom", "Jane") # 값들에 이름을 부여
names(score) # score에 저장된 값들의 이름을 보이시오
## [1] "John" "Tom" "Jane"
score # 이름과 함께 값이 출력
## John Tom Jane
## 90 85 70d <- c(1, 4, 3, 7, 8)
d[1]
## [1] 1
d[2]
## [1] 4
d[3]
## [1] 3
d[4]
## [1] 7
d[5]
## [1] 8
d[6]
## [1] NA
d[c(2, 4)]
## [1] 4 7d <- c(1, 4, 3, 7, 8)
d[c(1, 3, 5)] # 1, 3, 5번째 값 출력
## [1] 1 3 8
d[1:3] # 처음 세 개의 값 출력
## [1] 1 4 3
d[seq(1, 5, 2)] # 홀수 번째 값 출력
## [1] 1 3 8
d[-2] # 2번째 값 제외하고 출력
## [1] 1 3 7 8
d[-c(3:5)] # 3~5번째 값은 제외하고 출력
## [1] 1 4GNP <- c(2000, 2450, 960)
GNP
## [1] 2000 2450 960
names(GNP) <- c("Korea", "Japan", "Nepal")
GNP
## Korea Japan Nepal
## 2000 2450 960
GNP[1]
## Korea
## 2000
GNP["Korea"]
## Korea
## 2000
GNP_NEW <- GNP[c("Korea", "Nepal")]
GNP_NEW
## Korea Nepal
## 2000 960v1 <- c(1, 5, 7, 8, 9)
v1
## [1] 1 5 7 8 9
v1[2] <- 3 # v1의 2번째 값을 3으로 변경
v1
## [1] 1 3 7 8 9
v1[c(1, 5)] <- c(10, 20) # v1의 1, 5번째 값을 각각 10, 20으로 변경
v1
## [1] 10 3 7 8 20d <- c(1, 4, 3, 7, 8)
2 * d
## [1] 2 8 6 14 16
d - 5
## [1] -4 -1 -2 2 3
3 * d + 4
## [1] 7 16 13 25 28x <- c(1, 2, 3)
y <- c(4, 5, 6)
x + y # 대응하는 원소끼리 더하여 출력
## [1] 5 7 9
x * y # 대응하는 원소끼리 곱하여 출력
## [1] 4 10 18
z <- x + y # x, y를 더하여 z에 저장
z
## [1] 5 7 9d <- c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
sum(d) # d에 포함된 값들의 합
## [1] 55
sum(2 * d) # d에 포함된 값들에 2를 곱한 후 합한 값
## [1] 110
length(d) # d에 포함된 값들의 개수
## [1] 10
mean(d[1:5]) # 1~5번째 값들의 평균
## [1] 3
max(d) # d에 포함된 값들의 최댓값
## [1] 10
min(d) # d에 포함된 값들의 최솟값
## [1] 1
sort(d) # 오름차순 정렬
## [1] 1 2 3 4 5 6 7 8 9 10
sort(d, decreasing = FALSE) # 오름차순 정렬
## [1] 1 2 3 4 5 6 7 8 9 10
sort(d, decreasing = TRUE) # 내림차순 정렬
## [1] 10 9 8 7 6 5 4 3 2 1
sort(d, TRUE) # 내림차순 정렬
## [1] 10 9 8 7 6 5 4 3 2 1
v1 <- median(d)
v1
## [1] 5.5
v2 <- sum(d) / length(d)
v2
## [1] 5.5
mean(d)
## [1] 5.5d <- c(1, 2, 3, 4, 5, 6, 7, 8, 9)
d >= 5
## [1] FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE
d[d > 5] # 5보다 큰 값
## [1] 6 7 8 9
sum(d > 5) # 5보다 큰 값의 개수를 출력
## [1] 4
sum(d[d > 5]) # 5보다 큰 값의 합계를 출력
## [1] 30
d == 5
## [1] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
condi <- d > 5 & d < 8 # 조건을 변수에 저장
d[condi] # 조건에 맞는 값들을 선택
## [1] 6 7
d[d > 5 & d < 8]
## [1] 6 7ds <- c(90, 85, 70, 84)
my.info <- list(name = 'Tom', age = 60, status = TRUE, score = ds)
my.info # 리스트에 저장된 내용을 모두 출력
## $name
## [1] "Tom"
##
## $age
## [1] 60
##
## $status
## [1] TRUE
##
## $score
## [1] 90 85 70 84
my.info[1] # 이름이랑 내용 다 출력
## $name
## [1] "Tom"
my.info[[1]] # 리스트의 첫 번째 값을 출력
## [1] "Tom"
my.info$name # 리스트에서 값의 이름이 name인 값을 출력
## [1] "Tom"
my.info[[4]] # 리스트의 네 번째 값을 출력
## [1] 90 85 70 84bt <- c('A', 'B', 'B', 'O', 'AB', 'A') # 문자형 벡터 bt 정의
bt.new <- factor(bt) # 팩터 bt.new 정의
bt # 벡터 bt의 내용 출력
## [1] "A" "B" "B" "O" "AB" "A"
bt.new # 팩터 bt.new의 내용 출력
## [1] A B B O AB A
## Levels: A AB B O
bt[5] # 벡터 bt의 5번째 값 출력
## [1] "AB"
bt.new[5] # 팩터 bt.new의 5번째 값 출력
## [1] AB
## Levels: A AB B O
levels(bt.new) # 팩터에 저장된 값의 종류를 출력
## [1] "A" "AB" "B" "O"
as.integer(bt.new) # 팩터의 문자값을 숫자로 바꾸어 출력
## [1] 1 3 3 4 2 1
bt.new[7] <- 'B' # 팩터 bt.new의 7번째에 'B' 저장
bt.new[8] <- 'C' # 팩터 bt.new의 8번째에 'C' 저장
## Warning in `[<-.factor`(`*tmp*`, 8, value = "C"): invalid factor level, NA
## generated
bt.new # 팩터 bt.new의 내용 출력
## [1] A B B O AB A B <NA>
## Levels: A AB B O3장. 매트릭스와 데이터프레임
z <- matrix(1:20, nrow = 4, ncol = 5)
z # 매트릭스 z의 내용을 출력
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1 5 9 13 17
## [2,] 2 6 10 14 18
## [3,] 3 7 11 15 19
## [4,] 4 8 12 16 20z2 <- matrix(1:20, nrow = 4, ncol = 5, byrow = T)
z2 # 매트릭스 z2의 내용을 출력
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1 2 3 4 5
## [2,] 6 7 8 9 10
## [3,] 11 12 13 14 15
## [4,] 16 17 18 19 20
z <- matrix(1:16, nrow = 4, ncol = 5)
## Warning in matrix(1:16, nrow = 4, ncol = 5): data length [16] is not a
## sub-multiple or multiple of the number of columns [5]
z
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1 5 9 13 1
## [2,] 2 6 10 14 2
## [3,] 3 7 11 15 3
## [4,] 4 8 12 16 4x <- 1:4 # 벡터 x 생성
y <- 5:8 # 벡터 y 생성
z <- matrix(1:20, nrow = 4, ncol = 5) # 매트릭스 z 생성
m1 <- cbind(x, y) # x와 y를 열 방향으로 결합하여 매트릭스 생성
m1 # 매트릭스 m1의 내용을 출력
## x y
## [1,] 1 5
## [2,] 2 6
## [3,] 3 7
## [4,] 4 8
m2 <- rbind(x, y) # x와 y를 행 방향으로 결합하여 매트릭스 생성
m2 # 매트릭스 m2의 내용을 출력
## [,1] [,2] [,3] [,4]
## x 1 2 3 4
## y 5 6 7 8
m3 <- rbind(m2, x) # m2와 벡터 x를 행 방향으로 결합
m3 # 매트릭스 m3의 내용을 출력
## [,1] [,2] [,3] [,4]
## x 1 2 3 4
## y 5 6 7 8
## x 1 2 3 4
m4 <- cbind(z, x) # 매트릭스 z와 벡터 x를 열 방향으로 결합
m4 # 매트릭스 m4의 내용을 출력
## x
## [1,] 1 5 9 13 17 1
## [2,] 2 6 10 14 18 2
## [3,] 3 7 11 15 19 3
## [4,] 4 8 12 16 20 4
x <- 1:5
m5 <- cbind(z, x)
## Warning in cbind(z, x): number of rows of result is not a multiple of vector
## length (arg 2)
m5
## x
## [1,] 1 5 9 13 17 1
## [2,] 2 6 10 14 18 2
## [3,] 3 7 11 15 19 3
## [4,] 4 8 12 16 20 4z <- matrix(1:20, nrow = 4, ncol = 5) # 매트릭스 z 생성
z # 매트릭스 z의 내용 출력
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1 5 9 13 17
## [2,] 2 6 10 14 18
## [3,] 3 7 11 15 19
## [4,] 4 8 12 16 20
z[2, 3] # 2행 3열에 있는 값
## [1] 10
z[1, 4] # 1행 4열에 있는 값
## [1] 13
z[2, ] # 2행에 있는 모든 값
## [1] 2 6 10 14 18
z[, 4] # 4열에 있는 모든 값
## [1] 13 14 15 16
z[, ]
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1 5 9 13 17
## [2,] 2 6 10 14 18
## [3,] 3 7 11 15 19
## [4,] 4 8 12 16 20
z
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1 5 9 13 17
## [2,] 2 6 10 14 18
## [3,] 3 7 11 15 19
## [4,] 4 8 12 16 20z <- matrix(1:20, nrow = 4, ncol = 5) # 매트릭스 z 생성
z # 매트릭스 z의 내용 출력
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1 5 9 13 17
## [2,] 2 6 10 14 18
## [3,] 3 7 11 15 19
## [4,] 4 8 12 16 20
z[2, 1:3] # 2행의 값 중 1~3열에 있는 값
## [1] 2 6 10
z[1, c(1, 2, 4)] # 1행의 값 중 1, 2, 4열에 있는 값
## [1] 1 5 13
z[1:2, ] # 1, 2행에 있는 모든 값
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1 5 9 13 17
## [2,] 2 6 10 14 18
z[, c(1, 4)] # 1, 4열에 있는 모든 값
## [,1] [,2]
## [1,] 1 13
## [2,] 2 14
## [3,] 3 15
## [4,] 4 16score <- matrix(c(90, 85, 69, 78,
85, 96, 49, 95,
90, 80, 70, 60),
nrow = 4,
ncol = 3)
score
## [,1] [,2] [,3]
## [1,] 90 85 90
## [2,] 85 96 80
## [3,] 69 49 70
## [4,] 78 95 60
rownames(score) <- c('John', 'Tom', 'Mark', 'Jane')
colnames(score) <- c('English', 'Math', 'Science')
score
## English Math Science
## John 90 85 90
## Tom 85 96 80
## Mark 69 49 70
## Jane 78 95 60score['John', 'Math'] # John의 수학 성적
## [1] 85
score['Tom', c('Math', 'Science')] # Tom의 수학, 과학 성적
## Math Science
## 96 80
score['Mark', ] # Mark의 모든 과목 성적
## English Math Science
## 69 49 70
score[, 'English'] # 모든 학생의 영어 성적
## John Tom Mark Jane
## 90 85 69 78
rownames(score) # score의 행의 이름
## [1] "John" "Tom" "Mark" "Jane"
colnames(score) # score의 열의 이름
## [1] "English" "Math" "Science"
colnames(score)[2] # score의 열의 이름 중 두 번째 값
## [1] "Math"city <- c("Seoul", "Tokyo", "Washington") # 문자로 이루어진 벡터
rank <- c(1, 3, 2) # 숫자로 이루어진 벡터
city.info <- data.frame(city, rank) # 데이터프레임 생성
city.info # city.info의 내용 출력
## city rank
## 1 Seoul 1
## 2 Tokyo 3
## 3 Washington 2iris
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
## 7 4.6 3.4 1.4 0.3 setosa
## 8 5.0 3.4 1.5 0.2 setosa
## 9 4.4 2.9 1.4 0.2 setosa
## 10 4.9 3.1 1.5 0.1 setosa
## 11 5.4 3.7 1.5 0.2 setosa
## 12 4.8 3.4 1.6 0.2 setosa
## 13 4.8 3.0 1.4 0.1 setosa
## 14 4.3 3.0 1.1 0.1 setosa
## 15 5.8 4.0 1.2 0.2 setosa
## 16 5.7 4.4 1.5 0.4 setosa
## 17 5.4 3.9 1.3 0.4 setosa
## 18 5.1 3.5 1.4 0.3 setosa
## 19 5.7 3.8 1.7 0.3 setosa
## 20 5.1 3.8 1.5 0.3 setosa
## 21 5.4 3.4 1.7 0.2 setosa
## 22 5.1 3.7 1.5 0.4 setosa
## 23 4.6 3.6 1.0 0.2 setosa
## 24 5.1 3.3 1.7 0.5 setosa
## 25 4.8 3.4 1.9 0.2 setosa
## 26 5.0 3.0 1.6 0.2 setosa
## 27 5.0 3.4 1.6 0.4 setosa
## 28 5.2 3.5 1.5 0.2 setosa
## 29 5.2 3.4 1.4 0.2 setosa
## 30 4.7 3.2 1.6 0.2 setosa
## 31 4.8 3.1 1.6 0.2 setosa
## 32 5.4 3.4 1.5 0.4 setosa
## 33 5.2 4.1 1.5 0.1 setosa
## 34 5.5 4.2 1.4 0.2 setosa
## 35 4.9 3.1 1.5 0.2 setosa
## 36 5.0 3.2 1.2 0.2 setosa
## 37 5.5 3.5 1.3 0.2 setosa
## 38 4.9 3.6 1.4 0.1 setosa
## 39 4.4 3.0 1.3 0.2 setosa
## 40 5.1 3.4 1.5 0.2 setosa
## 41 5.0 3.5 1.3 0.3 setosa
## 42 4.5 2.3 1.3 0.3 setosa
## 43 4.4 3.2 1.3 0.2 setosa
## 44 5.0 3.5 1.6 0.6 setosa
## 45 5.1 3.8 1.9 0.4 setosa
## 46 4.8 3.0 1.4 0.3 setosa
## 47 5.1 3.8 1.6 0.2 setosa
## 48 4.6 3.2 1.4 0.2 setosa
## 49 5.3 3.7 1.5 0.2 setosa
## 50 5.0 3.3 1.4 0.2 setosa
## 51 7.0 3.2 4.7 1.4 versicolor
## 52 6.4 3.2 4.5 1.5 versicolor
## 53 6.9 3.1 4.9 1.5 versicolor
## 54 5.5 2.3 4.0 1.3 versicolor
## 55 6.5 2.8 4.6 1.5 versicolor
## 56 5.7 2.8 4.5 1.3 versicolor
## 57 6.3 3.3 4.7 1.6 versicolor
## 58 4.9 2.4 3.3 1.0 versicolor
## 59 6.6 2.9 4.6 1.3 versicolor
## 60 5.2 2.7 3.9 1.4 versicolor
## 61 5.0 2.0 3.5 1.0 versicolor
## 62 5.9 3.0 4.2 1.5 versicolor
## 63 6.0 2.2 4.0 1.0 versicolor
## 64 6.1 2.9 4.7 1.4 versicolor
## 65 5.6 2.9 3.6 1.3 versicolor
## 66 6.7 3.1 4.4 1.4 versicolor
## 67 5.6 3.0 4.5 1.5 versicolor
## 68 5.8 2.7 4.1 1.0 versicolor
## 69 6.2 2.2 4.5 1.5 versicolor
## 70 5.6 2.5 3.9 1.1 versicolor
## 71 5.9 3.2 4.8 1.8 versicolor
## 72 6.1 2.8 4.0 1.3 versicolor
## 73 6.3 2.5 4.9 1.5 versicolor
## 74 6.1 2.8 4.7 1.2 versicolor
## 75 6.4 2.9 4.3 1.3 versicolor
## 76 6.6 3.0 4.4 1.4 versicolor
## 77 6.8 2.8 4.8 1.4 versicolor
## 78 6.7 3.0 5.0 1.7 versicolor
## 79 6.0 2.9 4.5 1.5 versicolor
## 80 5.7 2.6 3.5 1.0 versicolor
## 81 5.5 2.4 3.8 1.1 versicolor
## 82 5.5 2.4 3.7 1.0 versicolor
## 83 5.8 2.7 3.9 1.2 versicolor
## 84 6.0 2.7 5.1 1.6 versicolor
## 85 5.4 3.0 4.5 1.5 versicolor
## 86 6.0 3.4 4.5 1.6 versicolor
## 87 6.7 3.1 4.7 1.5 versicolor
## 88 6.3 2.3 4.4 1.3 versicolor
## 89 5.6 3.0 4.1 1.3 versicolor
## 90 5.5 2.5 4.0 1.3 versicolor
## 91 5.5 2.6 4.4 1.2 versicolor
## 92 6.1 3.0 4.6 1.4 versicolor
## 93 5.8 2.6 4.0 1.2 versicolor
## 94 5.0 2.3 3.3 1.0 versicolor
## 95 5.6 2.7 4.2 1.3 versicolor
## 96 5.7 3.0 4.2 1.2 versicolor
## 97 5.7 2.9 4.2 1.3 versicolor
## 98 6.2 2.9 4.3 1.3 versicolor
## 99 5.1 2.5 3.0 1.1 versicolor
## 100 5.7 2.8 4.1 1.3 versicolor
## 101 6.3 3.3 6.0 2.5 virginica
## 102 5.8 2.7 5.1 1.9 virginica
## 103 7.1 3.0 5.9 2.1 virginica
## 104 6.3 2.9 5.6 1.8 virginica
## 105 6.5 3.0 5.8 2.2 virginica
## 106 7.6 3.0 6.6 2.1 virginica
## 107 4.9 2.5 4.5 1.7 virginica
## 108 7.3 2.9 6.3 1.8 virginica
## 109 6.7 2.5 5.8 1.8 virginica
## 110 7.2 3.6 6.1 2.5 virginica
## 111 6.5 3.2 5.1 2.0 virginica
## 112 6.4 2.7 5.3 1.9 virginica
## 113 6.8 3.0 5.5 2.1 virginica
## 114 5.7 2.5 5.0 2.0 virginica
## 115 5.8 2.8 5.1 2.4 virginica
## 116 6.4 3.2 5.3 2.3 virginica
## 117 6.5 3.0 5.5 1.8 virginica
## 118 7.7 3.8 6.7 2.2 virginica
## 119 7.7 2.6 6.9 2.3 virginica
## 120 6.0 2.2 5.0 1.5 virginica
## 121 6.9 3.2 5.7 2.3 virginica
## 122 5.6 2.8 4.9 2.0 virginica
## 123 7.7 2.8 6.7 2.0 virginica
## 124 6.3 2.7 4.9 1.8 virginica
## 125 6.7 3.3 5.7 2.1 virginica
## 126 7.2 3.2 6.0 1.8 virginica
## 127 6.2 2.8 4.8 1.8 virginica
## 128 6.1 3.0 4.9 1.8 virginica
## 129 6.4 2.8 5.6 2.1 virginica
## 130 7.2 3.0 5.8 1.6 virginica
## 131 7.4 2.8 6.1 1.9 virginica
## 132 7.9 3.8 6.4 2.0 virginica
## 133 6.4 2.8 5.6 2.2 virginica
## 134 6.3 2.8 5.1 1.5 virginica
## 135 6.1 2.6 5.6 1.4 virginica
## 136 7.7 3.0 6.1 2.3 virginica
## 137 6.3 3.4 5.6 2.4 virginica
## 138 6.4 3.1 5.5 1.8 virginica
## 139 6.0 3.0 4.8 1.8 virginica
## 140 6.9 3.1 5.4 2.1 virginica
## 141 6.7 3.1 5.6 2.4 virginica
## 142 6.9 3.1 5.1 2.3 virginica
## 143 5.8 2.7 5.1 1.9 virginica
## 144 6.8 3.2 5.9 2.3 virginica
## 145 6.7 3.3 5.7 2.5 virginica
## 146 6.7 3.0 5.2 2.3 virginica
## 147 6.3 2.5 5.0 1.9 virginica
## 148 6.5 3.0 5.2 2.0 virginica
## 149 6.2 3.4 5.4 2.3 virginica
## 150 5.9 3.0 5.1 1.8 virginica
iris[, c(1:2)] # 1, 2열의 모든 데이터
## Sepal.Length Sepal.Width
## 1 5.1 3.5
## 2 4.9 3.0
## 3 4.7 3.2
## 4 4.6 3.1
## 5 5.0 3.6
## 6 5.4 3.9
## 7 4.6 3.4
## 8 5.0 3.4
## 9 4.4 2.9
## 10 4.9 3.1
## 11 5.4 3.7
## 12 4.8 3.4
## 13 4.8 3.0
## 14 4.3 3.0
## 15 5.8 4.0
## 16 5.7 4.4
## 17 5.4 3.9
## 18 5.1 3.5
## 19 5.7 3.8
## 20 5.1 3.8
## 21 5.4 3.4
## 22 5.1 3.7
## 23 4.6 3.6
## 24 5.1 3.3
## 25 4.8 3.4
## 26 5.0 3.0
## 27 5.0 3.4
## 28 5.2 3.5
## 29 5.2 3.4
## 30 4.7 3.2
## 31 4.8 3.1
## 32 5.4 3.4
## 33 5.2 4.1
## 34 5.5 4.2
## 35 4.9 3.1
## 36 5.0 3.2
## 37 5.5 3.5
## 38 4.9 3.6
## 39 4.4 3.0
## 40 5.1 3.4
## 41 5.0 3.5
## 42 4.5 2.3
## 43 4.4 3.2
## 44 5.0 3.5
## 45 5.1 3.8
## 46 4.8 3.0
## 47 5.1 3.8
## 48 4.6 3.2
## 49 5.3 3.7
## 50 5.0 3.3
## 51 7.0 3.2
## 52 6.4 3.2
## 53 6.9 3.1
## 54 5.5 2.3
## 55 6.5 2.8
## 56 5.7 2.8
## 57 6.3 3.3
## 58 4.9 2.4
## 59 6.6 2.9
## 60 5.2 2.7
## 61 5.0 2.0
## 62 5.9 3.0
## 63 6.0 2.2
## 64 6.1 2.9
## 65 5.6 2.9
## 66 6.7 3.1
## 67 5.6 3.0
## 68 5.8 2.7
## 69 6.2 2.2
## 70 5.6 2.5
## 71 5.9 3.2
## 72 6.1 2.8
## 73 6.3 2.5
## 74 6.1 2.8
## 75 6.4 2.9
## 76 6.6 3.0
## 77 6.8 2.8
## 78 6.7 3.0
## 79 6.0 2.9
## 80 5.7 2.6
## 81 5.5 2.4
## 82 5.5 2.4
## 83 5.8 2.7
## 84 6.0 2.7
## 85 5.4 3.0
## 86 6.0 3.4
## 87 6.7 3.1
## 88 6.3 2.3
## 89 5.6 3.0
## 90 5.5 2.5
## 91 5.5 2.6
## 92 6.1 3.0
## 93 5.8 2.6
## 94 5.0 2.3
## 95 5.6 2.7
## 96 5.7 3.0
## 97 5.7 2.9
## 98 6.2 2.9
## 99 5.1 2.5
## 100 5.7 2.8
## 101 6.3 3.3
## 102 5.8 2.7
## 103 7.1 3.0
## 104 6.3 2.9
## 105 6.5 3.0
## 106 7.6 3.0
## 107 4.9 2.5
## 108 7.3 2.9
## 109 6.7 2.5
## 110 7.2 3.6
## 111 6.5 3.2
## 112 6.4 2.7
## 113 6.8 3.0
## 114 5.7 2.5
## 115 5.8 2.8
## 116 6.4 3.2
## 117 6.5 3.0
## 118 7.7 3.8
## 119 7.7 2.6
## 120 6.0 2.2
## 121 6.9 3.2
## 122 5.6 2.8
## 123 7.7 2.8
## 124 6.3 2.7
## 125 6.7 3.3
## 126 7.2 3.2
## 127 6.2 2.8
## 128 6.1 3.0
## 129 6.4 2.8
## 130 7.2 3.0
## 131 7.4 2.8
## 132 7.9 3.8
## 133 6.4 2.8
## 134 6.3 2.8
## 135 6.1 2.6
## 136 7.7 3.0
## 137 6.3 3.4
## 138 6.4 3.1
## 139 6.0 3.0
## 140 6.9 3.1
## 141 6.7 3.1
## 142 6.9 3.1
## 143 5.8 2.7
## 144 6.8 3.2
## 145 6.7 3.3
## 146 6.7 3.0
## 147 6.3 2.5
## 148 6.5 3.0
## 149 6.2 3.4
## 150 5.9 3.0
iris[, c(1, 3, 5)] # 1, 3, 5열의 모든 데이터
## Sepal.Length Petal.Length Species
## 1 5.1 1.4 setosa
## 2 4.9 1.4 setosa
## 3 4.7 1.3 setosa
## 4 4.6 1.5 setosa
## 5 5.0 1.4 setosa
## 6 5.4 1.7 setosa
## 7 4.6 1.4 setosa
## 8 5.0 1.5 setosa
## 9 4.4 1.4 setosa
## 10 4.9 1.5 setosa
## 11 5.4 1.5 setosa
## 12 4.8 1.6 setosa
## 13 4.8 1.4 setosa
## 14 4.3 1.1 setosa
## 15 5.8 1.2 setosa
## 16 5.7 1.5 setosa
## 17 5.4 1.3 setosa
## 18 5.1 1.4 setosa
## 19 5.7 1.7 setosa
## 20 5.1 1.5 setosa
## 21 5.4 1.7 setosa
## 22 5.1 1.5 setosa
## 23 4.6 1.0 setosa
## 24 5.1 1.7 setosa
## 25 4.8 1.9 setosa
## 26 5.0 1.6 setosa
## 27 5.0 1.6 setosa
## 28 5.2 1.5 setosa
## 29 5.2 1.4 setosa
## 30 4.7 1.6 setosa
## 31 4.8 1.6 setosa
## 32 5.4 1.5 setosa
## 33 5.2 1.5 setosa
## 34 5.5 1.4 setosa
## 35 4.9 1.5 setosa
## 36 5.0 1.2 setosa
## 37 5.5 1.3 setosa
## 38 4.9 1.4 setosa
## 39 4.4 1.3 setosa
## 40 5.1 1.5 setosa
## 41 5.0 1.3 setosa
## 42 4.5 1.3 setosa
## 43 4.4 1.3 setosa
## 44 5.0 1.6 setosa
## 45 5.1 1.9 setosa
## 46 4.8 1.4 setosa
## 47 5.1 1.6 setosa
## 48 4.6 1.4 setosa
## 49 5.3 1.5 setosa
## 50 5.0 1.4 setosa
## 51 7.0 4.7 versicolor
## 52 6.4 4.5 versicolor
## 53 6.9 4.9 versicolor
## 54 5.5 4.0 versicolor
## 55 6.5 4.6 versicolor
## 56 5.7 4.5 versicolor
## 57 6.3 4.7 versicolor
## 58 4.9 3.3 versicolor
## 59 6.6 4.6 versicolor
## 60 5.2 3.9 versicolor
## 61 5.0 3.5 versicolor
## 62 5.9 4.2 versicolor
## 63 6.0 4.0 versicolor
## 64 6.1 4.7 versicolor
## 65 5.6 3.6 versicolor
## 66 6.7 4.4 versicolor
## 67 5.6 4.5 versicolor
## 68 5.8 4.1 versicolor
## 69 6.2 4.5 versicolor
## 70 5.6 3.9 versicolor
## 71 5.9 4.8 versicolor
## 72 6.1 4.0 versicolor
## 73 6.3 4.9 versicolor
## 74 6.1 4.7 versicolor
## 75 6.4 4.3 versicolor
## 76 6.6 4.4 versicolor
## 77 6.8 4.8 versicolor
## 78 6.7 5.0 versicolor
## 79 6.0 4.5 versicolor
## 80 5.7 3.5 versicolor
## 81 5.5 3.8 versicolor
## 82 5.5 3.7 versicolor
## 83 5.8 3.9 versicolor
## 84 6.0 5.1 versicolor
## 85 5.4 4.5 versicolor
## 86 6.0 4.5 versicolor
## 87 6.7 4.7 versicolor
## 88 6.3 4.4 versicolor
## 89 5.6 4.1 versicolor
## 90 5.5 4.0 versicolor
## 91 5.5 4.4 versicolor
## 92 6.1 4.6 versicolor
## 93 5.8 4.0 versicolor
## 94 5.0 3.3 versicolor
## 95 5.6 4.2 versicolor
## 96 5.7 4.2 versicolor
## 97 5.7 4.2 versicolor
## 98 6.2 4.3 versicolor
## 99 5.1 3.0 versicolor
## 100 5.7 4.1 versicolor
## 101 6.3 6.0 virginica
## 102 5.8 5.1 virginica
## 103 7.1 5.9 virginica
## 104 6.3 5.6 virginica
## 105 6.5 5.8 virginica
## 106 7.6 6.6 virginica
## 107 4.9 4.5 virginica
## 108 7.3 6.3 virginica
## 109 6.7 5.8 virginica
## 110 7.2 6.1 virginica
## 111 6.5 5.1 virginica
## 112 6.4 5.3 virginica
## 113 6.8 5.5 virginica
## 114 5.7 5.0 virginica
## 115 5.8 5.1 virginica
## 116 6.4 5.3 virginica
## 117 6.5 5.5 virginica
## 118 7.7 6.7 virginica
## 119 7.7 6.9 virginica
## 120 6.0 5.0 virginica
## 121 6.9 5.7 virginica
## 122 5.6 4.9 virginica
## 123 7.7 6.7 virginica
## 124 6.3 4.9 virginica
## 125 6.7 5.7 virginica
## 126 7.2 6.0 virginica
## 127 6.2 4.8 virginica
## 128 6.1 4.9 virginica
## 129 6.4 5.6 virginica
## 130 7.2 5.8 virginica
## 131 7.4 6.1 virginica
## 132 7.9 6.4 virginica
## 133 6.4 5.6 virginica
## 134 6.3 5.1 virginica
## 135 6.1 5.6 virginica
## 136 7.7 6.1 virginica
## 137 6.3 5.6 virginica
## 138 6.4 5.5 virginica
## 139 6.0 4.8 virginica
## 140 6.9 5.4 virginica
## 141 6.7 5.6 virginica
## 142 6.9 5.1 virginica
## 143 5.8 5.1 virginica
## 144 6.8 5.9 virginica
## 145 6.7 5.7 virginica
## 146 6.7 5.2 virginica
## 147 6.3 5.0 virginica
## 148 6.5 5.2 virginica
## 149 6.2 5.4 virginica
## 150 5.9 5.1 virginica
iris[, c("Sepal.Length", "Species")] # 1, 5열의 모든 데이터
## Sepal.Length Species
## 1 5.1 setosa
## 2 4.9 setosa
## 3 4.7 setosa
## 4 4.6 setosa
## 5 5.0 setosa
## 6 5.4 setosa
## 7 4.6 setosa
## 8 5.0 setosa
## 9 4.4 setosa
## 10 4.9 setosa
## 11 5.4 setosa
## 12 4.8 setosa
## 13 4.8 setosa
## 14 4.3 setosa
## 15 5.8 setosa
## 16 5.7 setosa
## 17 5.4 setosa
## 18 5.1 setosa
## 19 5.7 setosa
## 20 5.1 setosa
## 21 5.4 setosa
## 22 5.1 setosa
## 23 4.6 setosa
## 24 5.1 setosa
## 25 4.8 setosa
## 26 5.0 setosa
## 27 5.0 setosa
## 28 5.2 setosa
## 29 5.2 setosa
## 30 4.7 setosa
## 31 4.8 setosa
## 32 5.4 setosa
## 33 5.2 setosa
## 34 5.5 setosa
## 35 4.9 setosa
## 36 5.0 setosa
## 37 5.5 setosa
## 38 4.9 setosa
## 39 4.4 setosa
## 40 5.1 setosa
## 41 5.0 setosa
## 42 4.5 setosa
## 43 4.4 setosa
## 44 5.0 setosa
## 45 5.1 setosa
## 46 4.8 setosa
## 47 5.1 setosa
## 48 4.6 setosa
## 49 5.3 setosa
## 50 5.0 setosa
## 51 7.0 versicolor
## 52 6.4 versicolor
## 53 6.9 versicolor
## 54 5.5 versicolor
## 55 6.5 versicolor
## 56 5.7 versicolor
## 57 6.3 versicolor
## 58 4.9 versicolor
## 59 6.6 versicolor
## 60 5.2 versicolor
## 61 5.0 versicolor
## 62 5.9 versicolor
## 63 6.0 versicolor
## 64 6.1 versicolor
## 65 5.6 versicolor
## 66 6.7 versicolor
## 67 5.6 versicolor
## 68 5.8 versicolor
## 69 6.2 versicolor
## 70 5.6 versicolor
## 71 5.9 versicolor
## 72 6.1 versicolor
## 73 6.3 versicolor
## 74 6.1 versicolor
## 75 6.4 versicolor
## 76 6.6 versicolor
## 77 6.8 versicolor
## 78 6.7 versicolor
## 79 6.0 versicolor
## 80 5.7 versicolor
## 81 5.5 versicolor
## 82 5.5 versicolor
## 83 5.8 versicolor
## 84 6.0 versicolor
## 85 5.4 versicolor
## 86 6.0 versicolor
## 87 6.7 versicolor
## 88 6.3 versicolor
## 89 5.6 versicolor
## 90 5.5 versicolor
## 91 5.5 versicolor
## 92 6.1 versicolor
## 93 5.8 versicolor
## 94 5.0 versicolor
## 95 5.6 versicolor
## 96 5.7 versicolor
## 97 5.7 versicolor
## 98 6.2 versicolor
## 99 5.1 versicolor
## 100 5.7 versicolor
## 101 6.3 virginica
## 102 5.8 virginica
## 103 7.1 virginica
## 104 6.3 virginica
## 105 6.5 virginica
## 106 7.6 virginica
## 107 4.9 virginica
## 108 7.3 virginica
## 109 6.7 virginica
## 110 7.2 virginica
## 111 6.5 virginica
## 112 6.4 virginica
## 113 6.8 virginica
## 114 5.7 virginica
## 115 5.8 virginica
## 116 6.4 virginica
## 117 6.5 virginica
## 118 7.7 virginica
## 119 7.7 virginica
## 120 6.0 virginica
## 121 6.9 virginica
## 122 5.6 virginica
## 123 7.7 virginica
## 124 6.3 virginica
## 125 6.7 virginica
## 126 7.2 virginica
## 127 6.2 virginica
## 128 6.1 virginica
## 129 6.4 virginica
## 130 7.2 virginica
## 131 7.4 virginica
## 132 7.9 virginica
## 133 6.4 virginica
## 134 6.3 virginica
## 135 6.1 virginica
## 136 7.7 virginica
## 137 6.3 virginica
## 138 6.4 virginica
## 139 6.0 virginica
## 140 6.9 virginica
## 141 6.7 virginica
## 142 6.9 virginica
## 143 5.8 virginica
## 144 6.8 virginica
## 145 6.7 virginica
## 146 6.7 virginica
## 147 6.3 virginica
## 148 6.5 virginica
## 149 6.2 virginica
## 150 5.9 virginica
iris[1:5, ] # 1~5행의 모든 데이터
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
iris[1:5, c(1, 3)] # 1~5행의 데이터 중 1, 3열의 데이터
## Sepal.Length Petal.Length
## 1 5.1 1.4
## 2 4.9 1.4
## 3 4.7 1.3
## 4 4.6 1.5
## 5 5.0 1.4dim(iris) # 행과 열의 개수 출력
## [1] 150 5
nrow(iris) # 행의 개수 출력
## [1] 150
ncol(iris) # 열의 개수 출력
## [1] 5
colnames(iris) # 열 이름 출력, names()와 결과 동일
## [1] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width" "Species"
head(iris) # 데이터셋의 앞부분 일부 출력
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
tail(iris) # 데이터셋의 뒷부분 일부 출력
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 145 6.7 3.3 5.7 2.5 virginica
## 146 6.7 3.0 5.2 2.3 virginica
## 147 6.3 2.5 5.0 1.9 virginica
## 148 6.5 3.0 5.2 2.0 virginica
## 149 6.2 3.4 5.4 2.3 virginica
## 150 5.9 3.0 5.1 1.8 virginica
head(iris, 10)
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
## 7 4.6 3.4 1.4 0.3 setosa
## 8 5.0 3.4 1.5 0.2 setosa
## 9 4.4 2.9 1.4 0.2 setosa
## 10 4.9 3.1 1.5 0.1 setosa
tail(iris, 20)
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 131 7.4 2.8 6.1 1.9 virginica
## 132 7.9 3.8 6.4 2.0 virginica
## 133 6.4 2.8 5.6 2.2 virginica
## 134 6.3 2.8 5.1 1.5 virginica
## 135 6.1 2.6 5.6 1.4 virginica
## 136 7.7 3.0 6.1 2.3 virginica
## 137 6.3 3.4 5.6 2.4 virginica
## 138 6.4 3.1 5.5 1.8 virginica
## 139 6.0 3.0 4.8 1.8 virginica
## 140 6.9 3.1 5.4 2.1 virginica
## 141 6.7 3.1 5.6 2.4 virginica
## 142 6.9 3.1 5.1 2.3 virginica
## 143 5.8 2.7 5.1 1.9 virginica
## 144 6.8 3.2 5.9 2.3 virginica
## 145 6.7 3.3 5.7 2.5 virginica
## 146 6.7 3.0 5.2 2.3 virginica
## 147 6.3 2.5 5.0 1.9 virginica
## 148 6.5 3.0 5.2 2.0 virginica
## 149 6.2 3.4 5.4 2.3 virginica
## 150 5.9 3.0 5.1 1.8 virginicastr(iris) # 데이터셋 요약 정보 보기
## 'data.frame': 150 obs. of 5 variables:
## $ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
## $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
## $ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
## $ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
## $ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...
iris[, 5] # 품종 데이터 보기
## [1] setosa setosa setosa setosa setosa setosa
## [7] setosa setosa setosa setosa setosa setosa
## [13] setosa setosa setosa setosa setosa setosa
## [19] setosa setosa setosa setosa setosa setosa
## [25] setosa setosa setosa setosa setosa setosa
## [31] setosa setosa setosa setosa setosa setosa
## [37] setosa setosa setosa setosa setosa setosa
## [43] setosa setosa setosa setosa setosa setosa
## [49] setosa setosa versicolor versicolor versicolor versicolor
## [55] versicolor versicolor versicolor versicolor versicolor versicolor
## [61] versicolor versicolor versicolor versicolor versicolor versicolor
## [67] versicolor versicolor versicolor versicolor versicolor versicolor
## [73] versicolor versicolor versicolor versicolor versicolor versicolor
## [79] versicolor versicolor versicolor versicolor versicolor versicolor
## [85] versicolor versicolor versicolor versicolor versicolor versicolor
## [91] versicolor versicolor versicolor versicolor versicolor versicolor
## [97] versicolor versicolor versicolor versicolor virginica virginica
## [103] virginica virginica virginica virginica virginica virginica
## [109] virginica virginica virginica virginica virginica virginica
## [115] virginica virginica virginica virginica virginica virginica
## [121] virginica virginica virginica virginica virginica virginica
## [127] virginica virginica virginica virginica virginica virginica
## [133] virginica virginica virginica virginica virginica virginica
## [139] virginica virginica virginica virginica virginica virginica
## [145] virginica virginica virginica virginica virginica virginica
## Levels: setosa versicolor virginica
unique(iris[, 5]) # 품종의 종류 보기(중복 제거)
## [1] setosa versicolor virginica
## Levels: setosa versicolor virginica
table(iris[, "Species"]) # 품종의 종류별 행의 개수 세기
##
## setosa versicolor virginica
## 50 50 50iris[, -5]
## Sepal.Length Sepal.Width Petal.Length Petal.Width
## 1 5.1 3.5 1.4 0.2
## 2 4.9 3.0 1.4 0.2
## 3 4.7 3.2 1.3 0.2
## 4 4.6 3.1 1.5 0.2
## 5 5.0 3.6 1.4 0.2
## 6 5.4 3.9 1.7 0.4
## 7 4.6 3.4 1.4 0.3
## 8 5.0 3.4 1.5 0.2
## 9 4.4 2.9 1.4 0.2
## 10 4.9 3.1 1.5 0.1
## 11 5.4 3.7 1.5 0.2
## 12 4.8 3.4 1.6 0.2
## 13 4.8 3.0 1.4 0.1
## 14 4.3 3.0 1.1 0.1
## 15 5.8 4.0 1.2 0.2
## 16 5.7 4.4 1.5 0.4
## 17 5.4 3.9 1.3 0.4
## 18 5.1 3.5 1.4 0.3
## 19 5.7 3.8 1.7 0.3
## 20 5.1 3.8 1.5 0.3
## 21 5.4 3.4 1.7 0.2
## 22 5.1 3.7 1.5 0.4
## 23 4.6 3.6 1.0 0.2
## 24 5.1 3.3 1.7 0.5
## 25 4.8 3.4 1.9 0.2
## 26 5.0 3.0 1.6 0.2
## 27 5.0 3.4 1.6 0.4
## 28 5.2 3.5 1.5 0.2
## 29 5.2 3.4 1.4 0.2
## 30 4.7 3.2 1.6 0.2
## 31 4.8 3.1 1.6 0.2
## 32 5.4 3.4 1.5 0.4
## 33 5.2 4.1 1.5 0.1
## 34 5.5 4.2 1.4 0.2
## 35 4.9 3.1 1.5 0.2
## 36 5.0 3.2 1.2 0.2
## 37 5.5 3.5 1.3 0.2
## 38 4.9 3.6 1.4 0.1
## 39 4.4 3.0 1.3 0.2
## 40 5.1 3.4 1.5 0.2
## 41 5.0 3.5 1.3 0.3
## 42 4.5 2.3 1.3 0.3
## 43 4.4 3.2 1.3 0.2
## 44 5.0 3.5 1.6 0.6
## 45 5.1 3.8 1.9 0.4
## 46 4.8 3.0 1.4 0.3
## 47 5.1 3.8 1.6 0.2
## 48 4.6 3.2 1.4 0.2
## 49 5.3 3.7 1.5 0.2
## 50 5.0 3.3 1.4 0.2
## 51 7.0 3.2 4.7 1.4
## 52 6.4 3.2 4.5 1.5
## 53 6.9 3.1 4.9 1.5
## 54 5.5 2.3 4.0 1.3
## 55 6.5 2.8 4.6 1.5
## 56 5.7 2.8 4.5 1.3
## 57 6.3 3.3 4.7 1.6
## 58 4.9 2.4 3.3 1.0
## 59 6.6 2.9 4.6 1.3
## 60 5.2 2.7 3.9 1.4
## 61 5.0 2.0 3.5 1.0
## 62 5.9 3.0 4.2 1.5
## 63 6.0 2.2 4.0 1.0
## 64 6.1 2.9 4.7 1.4
## 65 5.6 2.9 3.6 1.3
## 66 6.7 3.1 4.4 1.4
## 67 5.6 3.0 4.5 1.5
## 68 5.8 2.7 4.1 1.0
## 69 6.2 2.2 4.5 1.5
## 70 5.6 2.5 3.9 1.1
## 71 5.9 3.2 4.8 1.8
## 72 6.1 2.8 4.0 1.3
## 73 6.3 2.5 4.9 1.5
## 74 6.1 2.8 4.7 1.2
## 75 6.4 2.9 4.3 1.3
## 76 6.6 3.0 4.4 1.4
## 77 6.8 2.8 4.8 1.4
## 78 6.7 3.0 5.0 1.7
## 79 6.0 2.9 4.5 1.5
## 80 5.7 2.6 3.5 1.0
## 81 5.5 2.4 3.8 1.1
## 82 5.5 2.4 3.7 1.0
## 83 5.8 2.7 3.9 1.2
## 84 6.0 2.7 5.1 1.6
## 85 5.4 3.0 4.5 1.5
## 86 6.0 3.4 4.5 1.6
## 87 6.7 3.1 4.7 1.5
## 88 6.3 2.3 4.4 1.3
## 89 5.6 3.0 4.1 1.3
## 90 5.5 2.5 4.0 1.3
## 91 5.5 2.6 4.4 1.2
## 92 6.1 3.0 4.6 1.4
## 93 5.8 2.6 4.0 1.2
## 94 5.0 2.3 3.3 1.0
## 95 5.6 2.7 4.2 1.3
## 96 5.7 3.0 4.2 1.2
## 97 5.7 2.9 4.2 1.3
## 98 6.2 2.9 4.3 1.3
## 99 5.1 2.5 3.0 1.1
## 100 5.7 2.8 4.1 1.3
## 101 6.3 3.3 6.0 2.5
## 102 5.8 2.7 5.1 1.9
## 103 7.1 3.0 5.9 2.1
## 104 6.3 2.9 5.6 1.8
## 105 6.5 3.0 5.8 2.2
## 106 7.6 3.0 6.6 2.1
## 107 4.9 2.5 4.5 1.7
## 108 7.3 2.9 6.3 1.8
## 109 6.7 2.5 5.8 1.8
## 110 7.2 3.6 6.1 2.5
## 111 6.5 3.2 5.1 2.0
## 112 6.4 2.7 5.3 1.9
## 113 6.8 3.0 5.5 2.1
## 114 5.7 2.5 5.0 2.0
## 115 5.8 2.8 5.1 2.4
## 116 6.4 3.2 5.3 2.3
## 117 6.5 3.0 5.5 1.8
## 118 7.7 3.8 6.7 2.2
## 119 7.7 2.6 6.9 2.3
## 120 6.0 2.2 5.0 1.5
## 121 6.9 3.2 5.7 2.3
## 122 5.6 2.8 4.9 2.0
## 123 7.7 2.8 6.7 2.0
## 124 6.3 2.7 4.9 1.8
## 125 6.7 3.3 5.7 2.1
## 126 7.2 3.2 6.0 1.8
## 127 6.2 2.8 4.8 1.8
## 128 6.1 3.0 4.9 1.8
## 129 6.4 2.8 5.6 2.1
## 130 7.2 3.0 5.8 1.6
## 131 7.4 2.8 6.1 1.9
## 132 7.9 3.8 6.4 2.0
## 133 6.4 2.8 5.6 2.2
## 134 6.3 2.8 5.1 1.5
## 135 6.1 2.6 5.6 1.4
## 136 7.7 3.0 6.1 2.3
## 137 6.3 3.4 5.6 2.4
## 138 6.4 3.1 5.5 1.8
## 139 6.0 3.0 4.8 1.8
## 140 6.9 3.1 5.4 2.1
## 141 6.7 3.1 5.6 2.4
## 142 6.9 3.1 5.1 2.3
## 143 5.8 2.7 5.1 1.9
## 144 6.8 3.2 5.9 2.3
## 145 6.7 3.3 5.7 2.5
## 146 6.7 3.0 5.2 2.3
## 147 6.3 2.5 5.0 1.9
## 148 6.5 3.0 5.2 2.0
## 149 6.2 3.4 5.4 2.3
## 150 5.9 3.0 5.1 1.8
colSums(iris[, -5]) # 열별 합계
## Sepal.Length Sepal.Width Petal.Length Petal.Width
## 876.5 458.6 563.7 179.9
colMeans(iris[, -5]) # 열별 평균
## Sepal.Length Sepal.Width Petal.Length Petal.Width
## 5.843333 3.057333 3.758000 1.199333
rowSums(iris[, -5]) # 행별 합계
## [1] 10.2 9.5 9.4 9.4 10.2 11.4 9.7 10.1 8.9 9.6 10.8 10.0 9.3 8.5 11.2
## [16] 12.0 11.0 10.3 11.5 10.7 10.7 10.7 9.4 10.6 10.3 9.8 10.4 10.4 10.2 9.7
## [31] 9.7 10.7 10.9 11.3 9.7 9.6 10.5 10.0 8.9 10.2 10.1 8.4 9.1 10.7 11.2
## [46] 9.5 10.7 9.4 10.7 9.9 16.3 15.6 16.4 13.1 15.4 14.3 15.9 11.6 15.4 13.2
## [61] 11.5 14.6 13.2 15.1 13.4 15.6 14.6 13.6 14.4 13.1 15.7 14.2 15.2 14.8 14.9
## [76] 15.4 15.8 16.4 14.9 12.8 12.8 12.6 13.6 15.4 14.4 15.5 16.0 14.3 14.0 13.3
## [91] 13.7 15.1 13.6 11.6 13.8 14.1 14.1 14.7 11.7 13.9 18.1 15.5 18.1 16.6 17.5
## [106] 19.3 13.6 18.3 16.8 19.4 16.8 16.3 17.4 15.2 16.1 17.2 16.8 20.4 19.5 14.7
## [121] 18.1 15.3 19.2 15.7 17.8 18.2 15.6 15.8 16.9 17.6 18.2 20.1 17.0 15.7 15.7
## [136] 19.1 17.7 16.8 15.6 17.5 17.8 17.4 15.5 18.2 18.2 17.2 15.7 16.7 17.3 15.8
rowMeans(iris[, -5]) # 행별 평균
## [1] 2.550 2.375 2.350 2.350 2.550 2.850 2.425 2.525 2.225 2.400 2.700 2.500
## [13] 2.325 2.125 2.800 3.000 2.750 2.575 2.875 2.675 2.675 2.675 2.350 2.650
## [25] 2.575 2.450 2.600 2.600 2.550 2.425 2.425 2.675 2.725 2.825 2.425 2.400
## [37] 2.625 2.500 2.225 2.550 2.525 2.100 2.275 2.675 2.800 2.375 2.675 2.350
## [49] 2.675 2.475 4.075 3.900 4.100 3.275 3.850 3.575 3.975 2.900 3.850 3.300
## [61] 2.875 3.650 3.300 3.775 3.350 3.900 3.650 3.400 3.600 3.275 3.925 3.550
## [73] 3.800 3.700 3.725 3.850 3.950 4.100 3.725 3.200 3.200 3.150 3.400 3.850
## [85] 3.600 3.875 4.000 3.575 3.500 3.325 3.425 3.775 3.400 2.900 3.450 3.525
## [97] 3.525 3.675 2.925 3.475 4.525 3.875 4.525 4.150 4.375 4.825 3.400 4.575
## [109] 4.200 4.850 4.200 4.075 4.350 3.800 4.025 4.300 4.200 5.100 4.875 3.675
## [121] 4.525 3.825 4.800 3.925 4.450 4.550 3.900 3.950 4.225 4.400 4.550 5.025
## [133] 4.250 3.925 3.925 4.775 4.425 4.200 3.900 4.375 4.450 4.350 3.875 4.550
## [145] 4.550 4.300 3.925 4.175 4.325 3.950z <- matrix(1:20, nrow = 4, ncol = 5)
z
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1 5 9 13 17
## [2,] 2 6 10 14 18
## [3,] 3 7 11 15 19
## [4,] 4 8 12 16 20
t(z) # 행과열 방향 전환
## [,1] [,2] [,3] [,4]
## [1,] 1 2 3 4
## [2,] 5 6 7 8
## [3,] 9 10 11 12
## [4,] 13 14 15 16
## [5,] 17 18 19 20IR.1 <- subset(iris, Species == "setosa")
IR.1
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
## 7 4.6 3.4 1.4 0.3 setosa
## 8 5.0 3.4 1.5 0.2 setosa
## 9 4.4 2.9 1.4 0.2 setosa
## 10 4.9 3.1 1.5 0.1 setosa
## 11 5.4 3.7 1.5 0.2 setosa
## 12 4.8 3.4 1.6 0.2 setosa
## 13 4.8 3.0 1.4 0.1 setosa
## 14 4.3 3.0 1.1 0.1 setosa
## 15 5.8 4.0 1.2 0.2 setosa
## 16 5.7 4.4 1.5 0.4 setosa
## 17 5.4 3.9 1.3 0.4 setosa
## 18 5.1 3.5 1.4 0.3 setosa
## 19 5.7 3.8 1.7 0.3 setosa
## 20 5.1 3.8 1.5 0.3 setosa
## 21 5.4 3.4 1.7 0.2 setosa
## 22 5.1 3.7 1.5 0.4 setosa
## 23 4.6 3.6 1.0 0.2 setosa
## 24 5.1 3.3 1.7 0.5 setosa
## 25 4.8 3.4 1.9 0.2 setosa
## 26 5.0 3.0 1.6 0.2 setosa
## 27 5.0 3.4 1.6 0.4 setosa
## 28 5.2 3.5 1.5 0.2 setosa
## 29 5.2 3.4 1.4 0.2 setosa
## 30 4.7 3.2 1.6 0.2 setosa
## 31 4.8 3.1 1.6 0.2 setosa
## 32 5.4 3.4 1.5 0.4 setosa
## 33 5.2 4.1 1.5 0.1 setosa
## 34 5.5 4.2 1.4 0.2 setosa
## 35 4.9 3.1 1.5 0.2 setosa
## 36 5.0 3.2 1.2 0.2 setosa
## 37 5.5 3.5 1.3 0.2 setosa
## 38 4.9 3.6 1.4 0.1 setosa
## 39 4.4 3.0 1.3 0.2 setosa
## 40 5.1 3.4 1.5 0.2 setosa
## 41 5.0 3.5 1.3 0.3 setosa
## 42 4.5 2.3 1.3 0.3 setosa
## 43 4.4 3.2 1.3 0.2 setosa
## 44 5.0 3.5 1.6 0.6 setosa
## 45 5.1 3.8 1.9 0.4 setosa
## 46 4.8 3.0 1.4 0.3 setosa
## 47 5.1 3.8 1.6 0.2 setosa
## 48 4.6 3.2 1.4 0.2 setosa
## 49 5.3 3.7 1.5 0.2 setosa
## 50 5.0 3.3 1.4 0.2 setosa
IR.2 <- subset(iris, Sepal.Length > 5.0 & Sepal.Width > 4.0)
IR.2
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 16 5.7 4.4 1.5 0.4 setosa
## 33 5.2 4.1 1.5 0.1 setosa
## 34 5.5 4.2 1.4 0.2 setosa
IR.2[, c(2, 4)] # 2, 4열의 값만 추출
## Sepal.Width Petal.Width
## 16 4.4 0.4
## 33 4.1 0.1
## 34 4.2 0.2
IR.3 <- subset(iris, Sepal.Length > 5.0 | Sepal.Width > 4.0)
IR.3
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
## 11 5.4 3.7 1.5 0.2 setosa
## 15 5.8 4.0 1.2 0.2 setosa
## 16 5.7 4.4 1.5 0.4 setosa
## 17 5.4 3.9 1.3 0.4 setosa
## 18 5.1 3.5 1.4 0.3 setosa
## 19 5.7 3.8 1.7 0.3 setosa
## 20 5.1 3.8 1.5 0.3 setosa
## 21 5.4 3.4 1.7 0.2 setosa
## 22 5.1 3.7 1.5 0.4 setosa
## 24 5.1 3.3 1.7 0.5 setosa
## 28 5.2 3.5 1.5 0.2 setosa
## 29 5.2 3.4 1.4 0.2 setosa
## 32 5.4 3.4 1.5 0.4 setosa
## 33 5.2 4.1 1.5 0.1 setosa
## 34 5.5 4.2 1.4 0.2 setosa
## 37 5.5 3.5 1.3 0.2 setosa
## 40 5.1 3.4 1.5 0.2 setosa
## 45 5.1 3.8 1.9 0.4 setosa
## 47 5.1 3.8 1.6 0.2 setosa
## 49 5.3 3.7 1.5 0.2 setosa
## 51 7.0 3.2 4.7 1.4 versicolor
## 52 6.4 3.2 4.5 1.5 versicolor
## 53 6.9 3.1 4.9 1.5 versicolor
## 54 5.5 2.3 4.0 1.3 versicolor
## 55 6.5 2.8 4.6 1.5 versicolor
## 56 5.7 2.8 4.5 1.3 versicolor
## 57 6.3 3.3 4.7 1.6 versicolor
## 59 6.6 2.9 4.6 1.3 versicolor
## 60 5.2 2.7 3.9 1.4 versicolor
## 62 5.9 3.0 4.2 1.5 versicolor
## 63 6.0 2.2 4.0 1.0 versicolor
## 64 6.1 2.9 4.7 1.4 versicolor
## 65 5.6 2.9 3.6 1.3 versicolor
## 66 6.7 3.1 4.4 1.4 versicolor
## 67 5.6 3.0 4.5 1.5 versicolor
## 68 5.8 2.7 4.1 1.0 versicolor
## 69 6.2 2.2 4.5 1.5 versicolor
## 70 5.6 2.5 3.9 1.1 versicolor
## 71 5.9 3.2 4.8 1.8 versicolor
## 72 6.1 2.8 4.0 1.3 versicolor
## 73 6.3 2.5 4.9 1.5 versicolor
## 74 6.1 2.8 4.7 1.2 versicolor
## 75 6.4 2.9 4.3 1.3 versicolor
## 76 6.6 3.0 4.4 1.4 versicolor
## 77 6.8 2.8 4.8 1.4 versicolor
## 78 6.7 3.0 5.0 1.7 versicolor
## 79 6.0 2.9 4.5 1.5 versicolor
## 80 5.7 2.6 3.5 1.0 versicolor
## 81 5.5 2.4 3.8 1.1 versicolor
## 82 5.5 2.4 3.7 1.0 versicolor
## 83 5.8 2.7 3.9 1.2 versicolor
## 84 6.0 2.7 5.1 1.6 versicolor
## 85 5.4 3.0 4.5 1.5 versicolor
## 86 6.0 3.4 4.5 1.6 versicolor
## 87 6.7 3.1 4.7 1.5 versicolor
## 88 6.3 2.3 4.4 1.3 versicolor
## 89 5.6 3.0 4.1 1.3 versicolor
## 90 5.5 2.5 4.0 1.3 versicolor
## 91 5.5 2.6 4.4 1.2 versicolor
## 92 6.1 3.0 4.6 1.4 versicolor
## 93 5.8 2.6 4.0 1.2 versicolor
## 95 5.6 2.7 4.2 1.3 versicolor
## 96 5.7 3.0 4.2 1.2 versicolor
## 97 5.7 2.9 4.2 1.3 versicolor
## 98 6.2 2.9 4.3 1.3 versicolor
## 99 5.1 2.5 3.0 1.1 versicolor
## 100 5.7 2.8 4.1 1.3 versicolor
## 101 6.3 3.3 6.0 2.5 virginica
## 102 5.8 2.7 5.1 1.9 virginica
## 103 7.1 3.0 5.9 2.1 virginica
## 104 6.3 2.9 5.6 1.8 virginica
## 105 6.5 3.0 5.8 2.2 virginica
## 106 7.6 3.0 6.6 2.1 virginica
## 108 7.3 2.9 6.3 1.8 virginica
## 109 6.7 2.5 5.8 1.8 virginica
## 110 7.2 3.6 6.1 2.5 virginica
## 111 6.5 3.2 5.1 2.0 virginica
## 112 6.4 2.7 5.3 1.9 virginica
## 113 6.8 3.0 5.5 2.1 virginica
## 114 5.7 2.5 5.0 2.0 virginica
## 115 5.8 2.8 5.1 2.4 virginica
## 116 6.4 3.2 5.3 2.3 virginica
## 117 6.5 3.0 5.5 1.8 virginica
## 118 7.7 3.8 6.7 2.2 virginica
## 119 7.7 2.6 6.9 2.3 virginica
## 120 6.0 2.2 5.0 1.5 virginica
## 121 6.9 3.2 5.7 2.3 virginica
## 122 5.6 2.8 4.9 2.0 virginica
## 123 7.7 2.8 6.7 2.0 virginica
## 124 6.3 2.7 4.9 1.8 virginica
## 125 6.7 3.3 5.7 2.1 virginica
## 126 7.2 3.2 6.0 1.8 virginica
## 127 6.2 2.8 4.8 1.8 virginica
## 128 6.1 3.0 4.9 1.8 virginica
## 129 6.4 2.8 5.6 2.1 virginica
## 130 7.2 3.0 5.8 1.6 virginica
## 131 7.4 2.8 6.1 1.9 virginica
## 132 7.9 3.8 6.4 2.0 virginica
## 133 6.4 2.8 5.6 2.2 virginica
## 134 6.3 2.8 5.1 1.5 virginica
## 135 6.1 2.6 5.6 1.4 virginica
## 136 7.7 3.0 6.1 2.3 virginica
## 137 6.3 3.4 5.6 2.4 virginica
## 138 6.4 3.1 5.5 1.8 virginica
## 139 6.0 3.0 4.8 1.8 virginica
## 140 6.9 3.1 5.4 2.1 virginica
## 141 6.7 3.1 5.6 2.4 virginica
## 142 6.9 3.1 5.1 2.3 virginica
## 143 5.8 2.7 5.1 1.9 virginica
## 144 6.8 3.2 5.9 2.3 virginica
## 145 6.7 3.3 5.7 2.5 virginica
## 146 6.7 3.0 5.2 2.3 virginica
## 147 6.3 2.5 5.0 1.9 virginica
## 148 6.5 3.0 5.2 2.0 virginica
## 149 6.2 3.4 5.4 2.3 virginica
## 150 5.9 3.0 5.1 1.8 virginicaa <- matrix(1:20, 4, 5)
b <- matrix(21:40, 4, 5)
a ; b
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1 5 9 13 17
## [2,] 2 6 10 14 18
## [3,] 3 7 11 15 19
## [4,] 4 8 12 16 20
## [,1] [,2] [,3] [,4] [,5]
## [1,] 21 25 29 33 37
## [2,] 22 26 30 34 38
## [3,] 23 27 31 35 39
## [4,] 24 28 32 36 40
2 * a # 매트릭스 a에 저장된 값들에 2를 곱하기
## [,1] [,2] [,3] [,4] [,5]
## [1,] 2 10 18 26 34
## [2,] 4 12 20 28 36
## [3,] 6 14 22 30 38
## [4,] 8 16 24 32 40
b - 5
## [,1] [,2] [,3] [,4] [,5]
## [1,] 16 20 24 28 32
## [2,] 17 21 25 29 33
## [3,] 18 22 26 30 34
## [4,] 19 23 27 31 35
2 * a + 3 * b
## [,1] [,2] [,3] [,4] [,5]
## [1,] 65 85 105 125 145
## [2,] 70 90 110 130 150
## [3,] 75 95 115 135 155
## [4,] 80 100 120 140 160
a + b
## [,1] [,2] [,3] [,4] [,5]
## [1,] 22 30 38 46 54
## [2,] 24 32 40 48 56
## [3,] 26 34 42 50 58
## [4,] 28 36 44 52 60
b - a
## [,1] [,2] [,3] [,4] [,5]
## [1,] 20 20 20 20 20
## [2,] 20 20 20 20 20
## [3,] 20 20 20 20 20
## [4,] 20 20 20 20 20
b / a
## [,1] [,2] [,3] [,4] [,5]
## [1,] 21.000000 5.000000 3.222222 2.538462 2.176471
## [2,] 11.000000 4.333333 3.000000 2.428571 2.111111
## [3,] 7.666667 3.857143 2.818182 2.333333 2.052632
## [4,] 6.000000 3.500000 2.666667 2.250000 2.000000
a * b
## [,1] [,2] [,3] [,4] [,5]
## [1,] 21 125 261 429 629
## [2,] 44 156 300 476 684
## [3,] 69 189 341 525 741
## [4,] 96 224 384 576 800
a <- a * 3
a
## [,1] [,2] [,3] [,4] [,5]
## [1,] 3 15 27 39 51
## [2,] 6 18 30 42 54
## [3,] 9 21 33 45 57
## [4,] 12 24 36 48 60
b <- b - 5
b
## [,1] [,2] [,3] [,4] [,5]
## [1,] 16 20 24 28 32
## [2,] 17 21 25 29 33
## [3,] 18 22 26 30 34
## [4,] 19 23 27 31 35class(iris) # iris 데이터셋의 자료구조 확인
## [1] "data.frame"
class(state.x77) # state.x77 데이터셋의 자료구조 확인
## [1] "matrix" "array"
is.matrix(iris) # 데이터셋이 매트릭스인지를 확인하는 함수
## [1] FALSE
is.data.frame(iris) # 데이터셋이 데이터프레임인지를 확인하는 함수
## [1] TRUE
is.matrix(state.x77)
## [1] TRUE
is.data.frame(state.x77)
## [1] FALSE# 매트릭스를 데이터프레임으로 변환
st <- data.frame(state.x77)
head(st)
## Population Income Illiteracy Life.Exp Murder HS.Grad Frost Area
## Alabama 3615 3624 2.1 69.05 15.1 41.3 20 50708
## Alaska 365 6315 1.5 69.31 11.3 66.7 152 566432
## Arizona 2212 4530 1.8 70.55 7.8 58.1 15 113417
## Arkansas 2110 3378 1.9 70.66 10.1 39.9 65 51945
## California 21198 5114 1.1 71.71 10.3 62.6 20 156361
## Colorado 2541 4884 0.7 72.06 6.8 63.9 166 103766
class(st)
## [1] "data.frame"iris[, "Species"] # 결과=벡터. 매트릭스와 데이터프레임 모두 가능
## [1] setosa setosa setosa setosa setosa setosa
## [7] setosa setosa setosa setosa setosa setosa
## [13] setosa setosa setosa setosa setosa setosa
## [19] setosa setosa setosa setosa setosa setosa
## [25] setosa setosa setosa setosa setosa setosa
## [31] setosa setosa setosa setosa setosa setosa
## [37] setosa setosa setosa setosa setosa setosa
## [43] setosa setosa setosa setosa setosa setosa
## [49] setosa setosa versicolor versicolor versicolor versicolor
## [55] versicolor versicolor versicolor versicolor versicolor versicolor
## [61] versicolor versicolor versicolor versicolor versicolor versicolor
## [67] versicolor versicolor versicolor versicolor versicolor versicolor
## [73] versicolor versicolor versicolor versicolor versicolor versicolor
## [79] versicolor versicolor versicolor versicolor versicolor versicolor
## [85] versicolor versicolor versicolor versicolor versicolor versicolor
## [91] versicolor versicolor versicolor versicolor versicolor versicolor
## [97] versicolor versicolor versicolor versicolor virginica virginica
## [103] virginica virginica virginica virginica virginica virginica
## [109] virginica virginica virginica virginica virginica virginica
## [115] virginica virginica virginica virginica virginica virginica
## [121] virginica virginica virginica virginica virginica virginica
## [127] virginica virginica virginica virginica virginica virginica
## [133] virginica virginica virginica virginica virginica virginica
## [139] virginica virginica virginica virginica virginica virginica
## [145] virginica virginica virginica virginica virginica virginica
## Levels: setosa versicolor virginica
iris[, 5] # 결과=벡터. 매트릭스와 데이터프레임 모두 가능
## [1] setosa setosa setosa setosa setosa setosa
## [7] setosa setosa setosa setosa setosa setosa
## [13] setosa setosa setosa setosa setosa setosa
## [19] setosa setosa setosa setosa setosa setosa
## [25] setosa setosa setosa setosa setosa setosa
## [31] setosa setosa setosa setosa setosa setosa
## [37] setosa setosa setosa setosa setosa setosa
## [43] setosa setosa setosa setosa setosa setosa
## [49] setosa setosa versicolor versicolor versicolor versicolor
## [55] versicolor versicolor versicolor versicolor versicolor versicolor
## [61] versicolor versicolor versicolor versicolor versicolor versicolor
## [67] versicolor versicolor versicolor versicolor versicolor versicolor
## [73] versicolor versicolor versicolor versicolor versicolor versicolor
## [79] versicolor versicolor versicolor versicolor versicolor versicolor
## [85] versicolor versicolor versicolor versicolor versicolor versicolor
## [91] versicolor versicolor versicolor versicolor versicolor versicolor
## [97] versicolor versicolor versicolor versicolor virginica virginica
## [103] virginica virginica virginica virginica virginica virginica
## [109] virginica virginica virginica virginica virginica virginica
## [115] virginica virginica virginica virginica virginica virginica
## [121] virginica virginica virginica virginica virginica virginica
## [127] virginica virginica virginica virginica virginica virginica
## [133] virginica virginica virginica virginica virginica virginica
## [139] virginica virginica virginica virginica virginica virginica
## [145] virginica virginica virginica virginica virginica virginica
## Levels: setosa versicolor virginica
iris["Species"] # 결과=데이터프레임. 데이터프레임만 가능
## Species
## 1 setosa
## 2 setosa
## 3 setosa
## 4 setosa
## 5 setosa
## 6 setosa
## 7 setosa
## 8 setosa
## 9 setosa
## 10 setosa
## 11 setosa
## 12 setosa
## 13 setosa
## 14 setosa
## 15 setosa
## 16 setosa
## 17 setosa
## 18 setosa
## 19 setosa
## 20 setosa
## 21 setosa
## 22 setosa
## 23 setosa
## 24 setosa
## 25 setosa
## 26 setosa
## 27 setosa
## 28 setosa
## 29 setosa
## 30 setosa
## 31 setosa
## 32 setosa
## 33 setosa
## 34 setosa
## 35 setosa
## 36 setosa
## 37 setosa
## 38 setosa
## 39 setosa
## 40 setosa
## 41 setosa
## 42 setosa
## 43 setosa
## 44 setosa
## 45 setosa
## 46 setosa
## 47 setosa
## 48 setosa
## 49 setosa
## 50 setosa
## 51 versicolor
## 52 versicolor
## 53 versicolor
## 54 versicolor
## 55 versicolor
## 56 versicolor
## 57 versicolor
## 58 versicolor
## 59 versicolor
## 60 versicolor
## 61 versicolor
## 62 versicolor
## 63 versicolor
## 64 versicolor
## 65 versicolor
## 66 versicolor
## 67 versicolor
## 68 versicolor
## 69 versicolor
## 70 versicolor
## 71 versicolor
## 72 versicolor
## 73 versicolor
## 74 versicolor
## 75 versicolor
## 76 versicolor
## 77 versicolor
## 78 versicolor
## 79 versicolor
## 80 versicolor
## 81 versicolor
## 82 versicolor
## 83 versicolor
## 84 versicolor
## 85 versicolor
## 86 versicolor
## 87 versicolor
## 88 versicolor
## 89 versicolor
## 90 versicolor
## 91 versicolor
## 92 versicolor
## 93 versicolor
## 94 versicolor
## 95 versicolor
## 96 versicolor
## 97 versicolor
## 98 versicolor
## 99 versicolor
## 100 versicolor
## 101 virginica
## 102 virginica
## 103 virginica
## 104 virginica
## 105 virginica
## 106 virginica
## 107 virginica
## 108 virginica
## 109 virginica
## 110 virginica
## 111 virginica
## 112 virginica
## 113 virginica
## 114 virginica
## 115 virginica
## 116 virginica
## 117 virginica
## 118 virginica
## 119 virginica
## 120 virginica
## 121 virginica
## 122 virginica
## 123 virginica
## 124 virginica
## 125 virginica
## 126 virginica
## 127 virginica
## 128 virginica
## 129 virginica
## 130 virginica
## 131 virginica
## 132 virginica
## 133 virginica
## 134 virginica
## 135 virginica
## 136 virginica
## 137 virginica
## 138 virginica
## 139 virginica
## 140 virginica
## 141 virginica
## 142 virginica
## 143 virginica
## 144 virginica
## 145 virginica
## 146 virginica
## 147 virginica
## 148 virginica
## 149 virginica
## 150 virginica
iris[5] # 결과=데이터프레임. 데이터프레임만 가능
## Species
## 1 setosa
## 2 setosa
## 3 setosa
## 4 setosa
## 5 setosa
## 6 setosa
## 7 setosa
## 8 setosa
## 9 setosa
## 10 setosa
## 11 setosa
## 12 setosa
## 13 setosa
## 14 setosa
## 15 setosa
## 16 setosa
## 17 setosa
## 18 setosa
## 19 setosa
## 20 setosa
## 21 setosa
## 22 setosa
## 23 setosa
## 24 setosa
## 25 setosa
## 26 setosa
## 27 setosa
## 28 setosa
## 29 setosa
## 30 setosa
## 31 setosa
## 32 setosa
## 33 setosa
## 34 setosa
## 35 setosa
## 36 setosa
## 37 setosa
## 38 setosa
## 39 setosa
## 40 setosa
## 41 setosa
## 42 setosa
## 43 setosa
## 44 setosa
## 45 setosa
## 46 setosa
## 47 setosa
## 48 setosa
## 49 setosa
## 50 setosa
## 51 versicolor
## 52 versicolor
## 53 versicolor
## 54 versicolor
## 55 versicolor
## 56 versicolor
## 57 versicolor
## 58 versicolor
## 59 versicolor
## 60 versicolor
## 61 versicolor
## 62 versicolor
## 63 versicolor
## 64 versicolor
## 65 versicolor
## 66 versicolor
## 67 versicolor
## 68 versicolor
## 69 versicolor
## 70 versicolor
## 71 versicolor
## 72 versicolor
## 73 versicolor
## 74 versicolor
## 75 versicolor
## 76 versicolor
## 77 versicolor
## 78 versicolor
## 79 versicolor
## 80 versicolor
## 81 versicolor
## 82 versicolor
## 83 versicolor
## 84 versicolor
## 85 versicolor
## 86 versicolor
## 87 versicolor
## 88 versicolor
## 89 versicolor
## 90 versicolor
## 91 versicolor
## 92 versicolor
## 93 versicolor
## 94 versicolor
## 95 versicolor
## 96 versicolor
## 97 versicolor
## 98 versicolor
## 99 versicolor
## 100 versicolor
## 101 virginica
## 102 virginica
## 103 virginica
## 104 virginica
## 105 virginica
## 106 virginica
## 107 virginica
## 108 virginica
## 109 virginica
## 110 virginica
## 111 virginica
## 112 virginica
## 113 virginica
## 114 virginica
## 115 virginica
## 116 virginica
## 117 virginica
## 118 virginica
## 119 virginica
## 120 virginica
## 121 virginica
## 122 virginica
## 123 virginica
## 124 virginica
## 125 virginica
## 126 virginica
## 127 virginica
## 128 virginica
## 129 virginica
## 130 virginica
## 131 virginica
## 132 virginica
## 133 virginica
## 134 virginica
## 135 virginica
## 136 virginica
## 137 virginica
## 138 virginica
## 139 virginica
## 140 virginica
## 141 virginica
## 142 virginica
## 143 virginica
## 144 virginica
## 145 virginica
## 146 virginica
## 147 virginica
## 148 virginica
## 149 virginica
## 150 virginica
iris$Species # 결과=벡터. 데이터프레임만 가능
## [1] setosa setosa setosa setosa setosa setosa
## [7] setosa setosa setosa setosa setosa setosa
## [13] setosa setosa setosa setosa setosa setosa
## [19] setosa setosa setosa setosa setosa setosa
## [25] setosa setosa setosa setosa setosa setosa
## [31] setosa setosa setosa setosa setosa setosa
## [37] setosa setosa setosa setosa setosa setosa
## [43] setosa setosa setosa setosa setosa setosa
## [49] setosa setosa versicolor versicolor versicolor versicolor
## [55] versicolor versicolor versicolor versicolor versicolor versicolor
## [61] versicolor versicolor versicolor versicolor versicolor versicolor
## [67] versicolor versicolor versicolor versicolor versicolor versicolor
## [73] versicolor versicolor versicolor versicolor versicolor versicolor
## [79] versicolor versicolor versicolor versicolor versicolor versicolor
## [85] versicolor versicolor versicolor versicolor versicolor versicolor
## [91] versicolor versicolor versicolor versicolor versicolor versicolor
## [97] versicolor versicolor versicolor versicolor virginica virginica
## [103] virginica virginica virginica virginica virginica virginica
## [109] virginica virginica virginica virginica virginica virginica
## [115] virginica virginica virginica virginica virginica virginica
## [121] virginica virginica virginica virginica virginica virginica
## [127] virginica virginica virginica virginica virginica virginica
## [133] virginica virginica virginica virginica virginica virginica
## [139] virginica virginica virginica virginica virginica virginica
## [145] virginica virginica virginica virginica virginica virginica
## Levels: setosa versicolor virginicagetwd()
## [1] "C:/Users/Hyunsoo Kim/Desktop/senior_grade/blog/my-quarto-website"
# setwd("G:/내 드라이브/202202/R_Basic/data") # 작업 폴더 지정
air <- read.csv("./R_Basic/data/airquality.csv", header = T) # .csv 파일 읽기
head(air)
## version.https...git.lfs.github.com.spec.v1
## 1 oid sha256:6fdc84af524856a54abe063336bfea6511e9fb5dfcd2ec6e1dfa9e1e4d8c7357
## 2 size 3044my.iris <- subset(iris, Species = 'Setosa') # Setosa 품종 데이터만 추출
## Warning: In subset.data.frame(iris, Species = "Setosa") :
## extra argument 'Species' will be disregarded
write.csv(my.iris, "./R_Basic/data/my_iris_1.csv") # .csv 파일에 저장하기4장. 조건문, 반복문, 함수
job.type <- 'A'
if (job.type == 'B') {
bonus <- 200 # 직무 유형이 B일 때 실행
} else {
bonus <- 100 # 직무 유형이 B가 아닌 나머지 경우 실행
}
print(bonus)
## [1] 100job.type <- 'B'
bonus <- 100
if (job.type == 'A') {
bonus <- 200 # 직무 유형이 A일 때 실행
}
print(bonus)
## [1] 100score <- 85
if (score > 90) {
grade <- 'A'
} else if (score > 80) {
grade <- 'B'
} else if (score > 70) {
grade <- 'C'
} else if (score > 60) {
grade <- 'D'
} else {
grade <- 'F'
}
print(grade)
## [1] "B"a <- 10
b <- 20
if (a > 5 & b > 5) { # and 사용
print(a + b)
}
## [1] 30
if (a > 5 | b > 30) { # or 사용
print(a * b)
}
## [1] 200
if (a > 5 & b > 30) {
print(a * b)
}
if (a > 20 | b > 30) {
print(a * b)
}
if (a > 20 & b > 15) {
print(a * b)
}
r_basic <- 70
python_basic <- 82
if (r_basic > 80 & python_basic > 80) {
grade <- "Excellent"
} else {
grade <- "Good"
}
grade
## [1] "Good"a <- 10
b <- 20
if (a > b) {
c <- a
} else {
c <- b
}
print(c)
## [1] 20
a <- 10
b <- 20
c <- ifelse(a > b, a, b)
print(c)
## [1] 20for(i in 1:5) {
print('*')
}
## [1] "*"
## [1] "*"
## [1] "*"
## [1] "*"
## [1] "*"
for (i in 1:5) {
print(i)
}
## [1] 1
## [1] 2
## [1] 3
## [1] 4
## [1] 5
for (i in 1:5) {
a <- i * 2
print(a)
}
## [1] 2
## [1] 4
## [1] 6
## [1] 8
## [1] 10
for (i in 1:10000) {
a <- i * 2
print(a)
}
## [1] 2
## [1] 4
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## [1] 19424
## [1] 19426
## [1] 19428
## [1] 19430
## [1] 19432
## [1] 19434
## [1] 19436
## [1] 19438
## [1] 19440
## [1] 19442
## [1] 19444
## [1] 19446
## [1] 19448
## [1] 19450
## [1] 19452
## [1] 19454
## [1] 19456
## [1] 19458
## [1] 19460
## [1] 19462
## [1] 19464
## [1] 19466
## [1] 19468
## [1] 19470
## [1] 19472
## [1] 19474
## [1] 19476
## [1] 19478
## [1] 19480
## [1] 19482
## [1] 19484
## [1] 19486
## [1] 19488
## [1] 19490
## [1] 19492
## [1] 19494
## [1] 19496
## [1] 19498
## [1] 19500
## [1] 19502
## [1] 19504
## [1] 19506
## [1] 19508
## [1] 19510
## [1] 19512
## [1] 19514
## [1] 19516
## [1] 19518
## [1] 19520
## [1] 19522
## [1] 19524
## [1] 19526
## [1] 19528
## [1] 19530
## [1] 19532
## [1] 19534
## [1] 19536
## [1] 19538
## [1] 19540
## [1] 19542
## [1] 19544
## [1] 19546
## [1] 19548
## [1] 19550
## [1] 19552
## [1] 19554
## [1] 19556
## [1] 19558
## [1] 19560
## [1] 19562
## [1] 19564
## [1] 19566
## [1] 19568
## [1] 19570
## [1] 19572
## [1] 19574
## [1] 19576
## [1] 19578
## [1] 19580
## [1] 19582
## [1] 19584
## [1] 19586
## [1] 19588
## [1] 19590
## [1] 19592
## [1] 19594
## [1] 19596
## [1] 19598
## [1] 19600
## [1] 19602
## [1] 19604
## [1] 19606
## [1] 19608
## [1] 19610
## [1] 19612
## [1] 19614
## [1] 19616
## [1] 19618
## [1] 19620
## [1] 19622
## [1] 19624
## [1] 19626
## [1] 19628
## [1] 19630
## [1] 19632
## [1] 19634
## [1] 19636
## [1] 19638
## [1] 19640
## [1] 19642
## [1] 19644
## [1] 19646
## [1] 19648
## [1] 19650
## [1] 19652
## [1] 19654
## [1] 19656
## [1] 19658
## [1] 19660
## [1] 19662
## [1] 19664
## [1] 19666
## [1] 19668
## [1] 19670
## [1] 19672
## [1] 19674
## [1] 19676
## [1] 19678
## [1] 19680
## [1] 19682
## [1] 19684
## [1] 19686
## [1] 19688
## [1] 19690
## [1] 19692
## [1] 19694
## [1] 19696
## [1] 19698
## [1] 19700
## [1] 19702
## [1] 19704
## [1] 19706
## [1] 19708
## [1] 19710
## [1] 19712
## [1] 19714
## [1] 19716
## [1] 19718
## [1] 19720
## [1] 19722
## [1] 19724
## [1] 19726
## [1] 19728
## [1] 19730
## [1] 19732
## [1] 19734
## [1] 19736
## [1] 19738
## [1] 19740
## [1] 19742
## [1] 19744
## [1] 19746
## [1] 19748
## [1] 19750
## [1] 19752
## [1] 19754
## [1] 19756
## [1] 19758
## [1] 19760
## [1] 19762
## [1] 19764
## [1] 19766
## [1] 19768
## [1] 19770
## [1] 19772
## [1] 19774
## [1] 19776
## [1] 19778
## [1] 19780
## [1] 19782
## [1] 19784
## [1] 19786
## [1] 19788
## [1] 19790
## [1] 19792
## [1] 19794
## [1] 19796
## [1] 19798
## [1] 19800
## [1] 19802
## [1] 19804
## [1] 19806
## [1] 19808
## [1] 19810
## [1] 19812
## [1] 19814
## [1] 19816
## [1] 19818
## [1] 19820
## [1] 19822
## [1] 19824
## [1] 19826
## [1] 19828
## [1] 19830
## [1] 19832
## [1] 19834
## [1] 19836
## [1] 19838
## [1] 19840
## [1] 19842
## [1] 19844
## [1] 19846
## [1] 19848
## [1] 19850
## [1] 19852
## [1] 19854
## [1] 19856
## [1] 19858
## [1] 19860
## [1] 19862
## [1] 19864
## [1] 19866
## [1] 19868
## [1] 19870
## [1] 19872
## [1] 19874
## [1] 19876
## [1] 19878
## [1] 19880
## [1] 19882
## [1] 19884
## [1] 19886
## [1] 19888
## [1] 19890
## [1] 19892
## [1] 19894
## [1] 19896
## [1] 19898
## [1] 19900
## [1] 19902
## [1] 19904
## [1] 19906
## [1] 19908
## [1] 19910
## [1] 19912
## [1] 19914
## [1] 19916
## [1] 19918
## [1] 19920
## [1] 19922
## [1] 19924
## [1] 19926
## [1] 19928
## [1] 19930
## [1] 19932
## [1] 19934
## [1] 19936
## [1] 19938
## [1] 19940
## [1] 19942
## [1] 19944
## [1] 19946
## [1] 19948
## [1] 19950
## [1] 19952
## [1] 19954
## [1] 19956
## [1] 19958
## [1] 19960
## [1] 19962
## [1] 19964
## [1] 19966
## [1] 19968
## [1] 19970
## [1] 19972
## [1] 19974
## [1] 19976
## [1] 19978
## [1] 19980
## [1] 19982
## [1] 19984
## [1] 19986
## [1] 19988
## [1] 19990
## [1] 19992
## [1] 19994
## [1] 19996
## [1] 19998
## [1] 20000
for (i in 1:10000) {
a <- i * 2 / 1521 + 10000
print(a)
}
## [1] 10000
## [1] 10000
## [1] 10000
## [1] 10000.01
## [1] 10000.01
## [1] 10000.01
## [1] 10000.01
## [1] 10000.01
## [1] 10000.01
## [1] 10000.01
## [1] 10000.01
## [1] 10000.02
## [1] 10000.02
## [1] 10000.02
## [1] 10000.02
## [1] 10000.02
## [1] 10000.02
## [1] 10000.02
## [1] 10000.02
## [1] 10000.03
## [1] 10000.03
## [1] 10000.03
## [1] 10000.03
## [1] 10000.03
## [1] 10000.03
## [1] 10000.03
## [1] 10000.04
## [1] 10000.04
## [1] 10000.04
## [1] 10000.04
## [1] 10000.04
## [1] 10000.04
## [1] 10000.04
## [1] 10000.04
## [1] 10000.05
## [1] 10000.05
## [1] 10000.05
## [1] 10000.05
## [1] 10000.05
## [1] 10000.05
## [1] 10000.05
## [1] 10000.06
## [1] 10000.06
## [1] 10000.06
## [1] 10000.06
## [1] 10000.06
## [1] 10000.06
## [1] 10000.06
## [1] 10000.06
## [1] 10000.07
## [1] 10000.07
## [1] 10000.07
## [1] 10000.07
## [1] 10000.07
## [1] 10000.07
## [1] 10000.07
## [1] 10000.07
## [1] 10000.08
## [1] 10000.08
## [1] 10000.08
## [1] 10000.08
## [1] 10000.08
## [1] 10000.08
## [1] 10000.08
## [1] 10000.09
## [1] 10000.09
## [1] 10000.09
## [1] 10000.09
## [1] 10000.09
## [1] 10000.09
## [1] 10000.09
## [1] 10000.09
## [1] 10000.1
## [1] 10000.1
## [1] 10000.1
## [1] 10000.1
## [1] 10000.1
## [1] 10000.1
## [1] 10000.1
## [1] 10000.11
## [1] 10000.11
## [1] 10000.11
## [1] 10000.11
## [1] 10000.11
## [1] 10000.11
## [1] 10000.11
## [1] 10000.11
## [1] 10000.12
## [1] 10000.12
## [1] 10000.12
## [1] 10000.12
## [1] 10000.12
## [1] 10000.12
## [1] 10000.12
## [1] 10000.12
## [1] 10000.13
## [1] 10000.13
## [1] 10000.13
## [1] 10000.13
## [1] 10000.13
## [1] 10000.13
## [1] 10000.13
## [1] 10000.14
## [1] 10000.14
## [1] 10000.14
## [1] 10000.14
## [1] 10000.14
## [1] 10000.14
## [1] 10000.14
## [1] 10000.14
## [1] 10000.15
## [1] 10000.15
## [1] 10000.15
## [1] 10000.15
## [1] 10000.15
## [1] 10000.15
## [1] 10000.15
## [1] 10000.16
## [1] 10000.16
## [1] 10000.16
## [1] 10000.16
## [1] 10000.16
## [1] 10000.16
## [1] 10000.16
## [1] 10000.16
## [1] 10000.17
## [1] 10000.17
## [1] 10000.17
## [1] 10000.17
## [1] 10000.17
## [1] 10000.17
## [1] 10000.17
## [1] 10000.17
## [1] 10000.18
## [1] 10000.18
## [1] 10000.18
## [1] 10000.18
## [1] 10000.18
## [1] 10000.18
## [1] 10000.18
## [1] 10000.19
## [1] 10000.19
## [1] 10000.19
## [1] 10000.19
## [1] 10000.19
## [1] 10000.19
## [1] 10000.19
## [1] 10000.19
## [1] 10000.2
## [1] 10000.2
## [1] 10000.2
## [1] 10000.2
## [1] 10000.2
## [1] 10000.2
## [1] 10000.2
## [1] 10000.21
## [1] 10000.21
## [1] 10000.21
## [1] 10000.21
## [1] 10000.21
## [1] 10000.21
## [1] 10000.21
## [1] 10000.21
## [1] 10000.22
## [1] 10000.22
## [1] 10000.22
## [1] 10000.22
## [1] 10000.22
## [1] 10000.22
## [1] 10000.22
## [1] 10000.22
## [1] 10000.23
## [1] 10000.23
## [1] 10000.23
## [1] 10000.23
## [1] 10000.23
## [1] 10000.23
## [1] 10000.23
## [1] 10000.24
## [1] 10000.24
## [1] 10000.24
## [1] 10000.24
## [1] 10000.24
## [1] 10000.24
## [1] 10000.24
## [1] 10000.24
## [1] 10000.25
## [1] 10000.25
## [1] 10000.25
## [1] 10000.25
## [1] 10000.25
## [1] 10000.25
## [1] 10000.25
## [1] 10000.26
## [1] 10000.26
## [1] 10000.26
## [1] 10000.26
## [1] 10000.26
## [1] 10000.26
## [1] 10000.26
## [1] 10000.26
## [1] 10000.27
## [1] 10000.27
## [1] 10000.27
## [1] 10000.27
## [1] 10000.27
## [1] 10000.27
## [1] 10000.27
## [1] 10000.27
## [1] 10000.28
## [1] 10000.28
## [1] 10000.28
## [1] 10000.28
## [1] 10000.28
## [1] 10000.28
## [1] 10000.28
## [1] 10000.29
## [1] 10000.29
## [1] 10000.29
## [1] 10000.29
## [1] 10000.29
## [1] 10000.29
## [1] 10000.29
## [1] 10000.29
## [1] 10000.3
## [1] 10000.3
## [1] 10000.3
## [1] 10000.3
## [1] 10000.3
## [1] 10000.3
## [1] 10000.3
## [1] 10000.31
## [1] 10000.31
## [1] 10000.31
## [1] 10000.31
## [1] 10000.31
## [1] 10000.31
## [1] 10000.31
## [1] 10000.31
## [1] 10000.32
## [1] 10000.32
## [1] 10000.32
## [1] 10000.32
## [1] 10000.32
## [1] 10000.32
## [1] 10000.32
## [1] 10000.32
## [1] 10000.33
## [1] 10000.33
## [1] 10000.33
## [1] 10000.33
## [1] 10000.33
## [1] 10000.33
## [1] 10000.33
## [1] 10000.34
## [1] 10000.34
## [1] 10000.34
## [1] 10000.34
## [1] 10000.34
## [1] 10000.34
## [1] 10000.34
## [1] 10000.34
## [1] 10000.35
## [1] 10000.35
## [1] 10000.35
## [1] 10000.35
## [1] 10000.35
## [1] 10000.35
## [1] 10000.35
## [1] 10000.36
## [1] 10000.36
## [1] 10000.36
## [1] 10000.36
## [1] 10000.36
## [1] 10000.36
## [1] 10000.36
## [1] 10000.36
## [1] 10000.37
## [1] 10000.37
## [1] 10000.37
## [1] 10000.37
## [1] 10000.37
## [1] 10000.37
## [1] 10000.37
## [1] 10000.37
## [1] 10000.38
## [1] 10000.38
## [1] 10000.38
## [1] 10000.38
## [1] 10000.38
## [1] 10000.38
## [1] 10000.38
## [1] 10000.39
## [1] 10000.39
## [1] 10000.39
## [1] 10000.39
## [1] 10000.39
## [1] 10000.39
## [1] 10000.39
## [1] 10000.39
## [1] 10000.4
## [1] 10000.4
## [1] 10000.4
## [1] 10000.4
## [1] 10000.4
## [1] 10000.4
## [1] 10000.4
## [1] 10000.4
## [1] 10000.41
## [1] 10000.41
## [1] 10000.41
## [1] 10000.41
## [1] 10000.41
## [1] 10000.41
## [1] 10000.41
## [1] 10000.42
## [1] 10000.42
## [1] 10000.42
## [1] 10000.42
## [1] 10000.42
## [1] 10000.42
## [1] 10000.42
## [1] 10000.42
## [1] 10000.43
## [1] 10000.43
## [1] 10000.43
## [1] 10000.43
## [1] 10000.43
## [1] 10000.43
## [1] 10000.43
## [1] 10000.44
## [1] 10000.44
## [1] 10000.44
## [1] 10000.44
## [1] 10000.44
## [1] 10000.44
## [1] 10000.44
## [1] 10000.44
## [1] 10000.45
## [1] 10000.45
## [1] 10000.45
## [1] 10000.45
## [1] 10000.45
## [1] 10000.45
## [1] 10000.45
## [1] 10000.45
## [1] 10000.46
## [1] 10000.46
## [1] 10000.46
## [1] 10000.46
## [1] 10000.46
## [1] 10000.46
## [1] 10000.46
## [1] 10000.47
## [1] 10000.47
## [1] 10000.47
## [1] 10000.47
## [1] 10000.47
## [1] 10000.47
## [1] 10000.47
## [1] 10000.47
## [1] 10000.48
## [1] 10000.48
## [1] 10000.48
## [1] 10000.48
## [1] 10000.48
## [1] 10000.48
## [1] 10000.48
## [1] 10000.49
## [1] 10000.49
## [1] 10000.49
## [1] 10000.49
## [1] 10000.49
## [1] 10000.49
## [1] 10000.49
## [1] 10000.49
## [1] 10000.5
## [1] 10000.5
## [1] 10000.5
## [1] 10000.5
## [1] 10000.5
## [1] 10000.5
## [1] 10000.5
## [1] 10000.5
## [1] 10000.51
## [1] 10000.51
## [1] 10000.51
## [1] 10000.51
## [1] 10000.51
## [1] 10000.51
## [1] 10000.51
## [1] 10000.52
## [1] 10000.52
## [1] 10000.52
## [1] 10000.52
## [1] 10000.52
## [1] 10000.52
## [1] 10000.52
## [1] 10000.52
## [1] 10000.53
## [1] 10000.53
## [1] 10000.53
## [1] 10000.53
## [1] 10000.53
## [1] 10000.53
## [1] 10000.53
## [1] 10000.54
## [1] 10000.54
## [1] 10000.54
## [1] 10000.54
## [1] 10000.54
## [1] 10000.54
## [1] 10000.54
## [1] 10000.54
## [1] 10000.55
## [1] 10000.55
## [1] 10000.55
## [1] 10000.55
## [1] 10000.55
## [1] 10000.55
## [1] 10000.55
## [1] 10000.55
## [1] 10000.56
## [1] 10000.56
## [1] 10000.56
## [1] 10000.56
## [1] 10000.56
## [1] 10000.56
## [1] 10000.56
## [1] 10000.57
## [1] 10000.57
## [1] 10000.57
## [1] 10000.57
## [1] 10000.57
## [1] 10000.57
## [1] 10000.57
## [1] 10000.57
## [1] 10000.58
## [1] 10000.58
## [1] 10000.58
## [1] 10000.58
## [1] 10000.58
## [1] 10000.58
## [1] 10000.58
## [1] 10000.59
## [1] 10000.59
## [1] 10000.59
## [1] 10000.59
## [1] 10000.59
## [1] 10000.59
## [1] 10000.59
## [1] 10000.59
## [1] 10000.6
## [1] 10000.6
## [1] 10000.6
## [1] 10000.6
## [1] 10000.6
## [1] 10000.6
## [1] 10000.6
## [1] 10000.6
## [1] 10000.61
## [1] 10000.61
## [1] 10000.61
## [1] 10000.61
## [1] 10000.61
## [1] 10000.61
## [1] 10000.61
## [1] 10000.62
## [1] 10000.62
## [1] 10000.62
## [1] 10000.62
## [1] 10000.62
## [1] 10000.62
## [1] 10000.62
## [1] 10000.62
## [1] 10000.63
## [1] 10000.63
## [1] 10000.63
## [1] 10000.63
## [1] 10000.63
## [1] 10000.63
## [1] 10000.63
## [1] 10000.64
## [1] 10000.64
## [1] 10000.64
## [1] 10000.64
## [1] 10000.64
## [1] 10000.64
## [1] 10000.64
## [1] 10000.64
## [1] 10000.65
## [1] 10000.65
## [1] 10000.65
## [1] 10000.65
## [1] 10000.65
## [1] 10000.65
## [1] 10000.65
## [1] 10000.65
## [1] 10000.66
## [1] 10000.66
## [1] 10000.66
## [1] 10000.66
## [1] 10000.66
## [1] 10000.66
## [1] 10000.66
## [1] 10000.67
## [1] 10000.67
## [1] 10000.67
## [1] 10000.67
## [1] 10000.67
## [1] 10000.67
## [1] 10000.67
## [1] 10000.67
## [1] 10000.68
## [1] 10000.68
## [1] 10000.68
## [1] 10000.68
## [1] 10000.68
## [1] 10000.68
## [1] 10000.68
## [1] 10000.69
## [1] 10000.69
## [1] 10000.69
## [1] 10000.69
## [1] 10000.69
## [1] 10000.69
## [1] 10000.69
## [1] 10000.69
## [1] 10000.7
## [1] 10000.7
## [1] 10000.7
## [1] 10000.7
## [1] 10000.7
## [1] 10000.7
## [1] 10000.7
## [1] 10000.7
## [1] 10000.71
## [1] 10000.71
## [1] 10000.71
## [1] 10000.71
## [1] 10000.71
## [1] 10000.71
## [1] 10000.71
## [1] 10000.72
## [1] 10000.72
## [1] 10000.72
## [1] 10000.72
## [1] 10000.72
## [1] 10000.72
## [1] 10000.72
## [1] 10000.72
## [1] 10000.73
## [1] 10000.73
## [1] 10000.73
## [1] 10000.73
## [1] 10000.73
## [1] 10000.73
## [1] 10000.73
## [1] 10000.74
## [1] 10000.74
## [1] 10000.74
## [1] 10000.74
## [1] 10000.74
## [1] 10000.74
## [1] 10000.74
## [1] 10000.74
## [1] 10000.75
## [1] 10000.75
## [1] 10000.75
## [1] 10000.75
## [1] 10000.75
## [1] 10000.75
## [1] 10000.75
## [1] 10000.75
## [1] 10000.76
## [1] 10000.76
## [1] 10000.76
## [1] 10000.76
## [1] 10000.76
## [1] 10000.76
## [1] 10000.76
## [1] 10000.77
## [1] 10000.77
## [1] 10000.77
## [1] 10000.77
## [1] 10000.77
## [1] 10000.77
## [1] 10000.77
## [1] 10000.77
## [1] 10000.78
## [1] 10000.78
## [1] 10000.78
## [1] 10000.78
## [1] 10000.78
## [1] 10000.78
## [1] 10000.78
## [1] 10000.79
## [1] 10000.79
## [1] 10000.79
## [1] 10000.79
## [1] 10000.79
## [1] 10000.79
## [1] 10000.79
## [1] 10000.79
## [1] 10000.8
## [1] 10000.8
## [1] 10000.8
## [1] 10000.8
## [1] 10000.8
## [1] 10000.8
## [1] 10000.8
## [1] 10000.8
## [1] 10000.81
## [1] 10000.81
## [1] 10000.81
## [1] 10000.81
## [1] 10000.81
## [1] 10000.81
## [1] 10000.81
## [1] 10000.82
## [1] 10000.82
## [1] 10000.82
## [1] 10000.82
## [1] 10000.82
## [1] 10000.82
## [1] 10000.82
## [1] 10000.82
## [1] 10000.83
## [1] 10000.83
## [1] 10000.83
## [1] 10000.83
## [1] 10000.83
## [1] 10000.83
## [1] 10000.83
## [1] 10000.83
## [1] 10000.84
## [1] 10000.84
## [1] 10000.84
## [1] 10000.84
## [1] 10000.84
## [1] 10000.84
## [1] 10000.84
## [1] 10000.85
## [1] 10000.85
## [1] 10000.85
## [1] 10000.85
## [1] 10000.85
## [1] 10000.85
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## [1] 10012.81
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## [1] 10013
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## [1] 10013.01
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## [1] 10013.09
## [1] 10013.1
## [1] 10013.1
## [1] 10013.1
## [1] 10013.1
## [1] 10013.1
## [1] 10013.1
## [1] 10013.1
## [1] 10013.1
## [1] 10013.11
## [1] 10013.11
## [1] 10013.11
## [1] 10013.11
## [1] 10013.11
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## [1] 10013.12
## [1] 10013.12
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## [1] 10013.13
## [1] 10013.13
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## [1] 10013.14
## [1] 10013.14
## [1] 10013.14
## [1] 10013.14
## [1] 10013.14
## [1] 10013.14
## [1] 10013.14
## [1] 10013.15
## [1] 10013.15
## [1] 10013.15
## [1] 10013.15for (i in 6:10) {
print(i)
}
## [1] 6
## [1] 7
## [1] 8
## [1] 9
## [1] 10for(i in 1:9) {
cat('2 *', i, '=', 2 * i, '\n')
}
## 2 * 1 = 2
## 2 * 2 = 4
## 2 * 3 = 6
## 2 * 4 = 8
## 2 * 5 = 10
## 2 * 6 = 12
## 2 * 7 = 14
## 2 * 8 = 16
## 2 * 9 = 18
for (i in 1:9) {
cat('2 *', i, '=', 2 * i)
}
## 2 * 1 = 22 * 2 = 42 * 3 = 62 * 4 = 82 * 5 = 102 * 6 = 122 * 7 = 142 * 8 = 162 * 9 = 18
for (i in 1:9) {
j <- i:10
print(j)
}
## [1] 1 2 3 4 5 6 7 8 9 10
## [1] 2 3 4 5 6 7 8 9 10
## [1] 3 4 5 6 7 8 9 10
## [1] 4 5 6 7 8 9 10
## [1] 5 6 7 8 9 10
## [1] 6 7 8 9 10
## [1] 7 8 9 10
## [1] 8 9 10
## [1] 9 10for(i in 1:20) {
if (i %% 2 == 0) { # 짝수인지 확인
print(i)
}
}
## [1] 2
## [1] 4
## [1] 6
## [1] 8
## [1] 10
## [1] 12
## [1] 14
## [1] 16
## [1] 18
## [1] 20sum <- 0
for (i in 1:100) {
sum <- sum + i # sum에 i 값을 누적
}
print(sum)
## [1] 5050
sum <- 0
for (i in 1:100) {
sum <- sum + i
print(c(sum, i))
}
## [1] 1 1
## [1] 3 2
## [1] 6 3
## [1] 10 4
## [1] 15 5
## [1] 21 6
## [1] 28 7
## [1] 36 8
## [1] 45 9
## [1] 55 10
## [1] 66 11
## [1] 78 12
## [1] 91 13
## [1] 105 14
## [1] 120 15
## [1] 136 16
## [1] 153 17
## [1] 171 18
## [1] 190 19
## [1] 210 20
## [1] 231 21
## [1] 253 22
## [1] 276 23
## [1] 300 24
## [1] 325 25
## [1] 351 26
## [1] 378 27
## [1] 406 28
## [1] 435 29
## [1] 465 30
## [1] 496 31
## [1] 528 32
## [1] 561 33
## [1] 595 34
## [1] 630 35
## [1] 666 36
## [1] 703 37
## [1] 741 38
## [1] 780 39
## [1] 820 40
## [1] 861 41
## [1] 903 42
## [1] 946 43
## [1] 990 44
## [1] 1035 45
## [1] 1081 46
## [1] 1128 47
## [1] 1176 48
## [1] 1225 49
## [1] 1275 50
## [1] 1326 51
## [1] 1378 52
## [1] 1431 53
## [1] 1485 54
## [1] 1540 55
## [1] 1596 56
## [1] 1653 57
## [1] 1711 58
## [1] 1770 59
## [1] 1830 60
## [1] 1891 61
## [1] 1953 62
## [1] 2016 63
## [1] 2080 64
## [1] 2145 65
## [1] 2211 66
## [1] 2278 67
## [1] 2346 68
## [1] 2415 69
## [1] 2485 70
## [1] 2556 71
## [1] 2628 72
## [1] 2701 73
## [1] 2775 74
## [1] 2850 75
## [1] 2926 76
## [1] 3003 77
## [1] 3081 78
## [1] 3160 79
## [1] 3240 80
## [1] 3321 81
## [1] 3403 82
## [1] 3486 83
## [1] 3570 84
## [1] 3655 85
## [1] 3741 86
## [1] 3828 87
## [1] 3916 88
## [1] 4005 89
## [1] 4095 90
## [1] 4186 91
## [1] 4278 92
## [1] 4371 93
## [1] 4465 94
## [1] 4560 95
## [1] 4656 96
## [1] 4753 97
## [1] 4851 98
## [1] 4950 99
## [1] 5050 100
print(sum)
## [1] 5050
sum <- 0
for (i in 1:100) {
print(c(sum, i))
sum <- sum + i
}
## [1] 0 1
## [1] 1 2
## [1] 3 3
## [1] 6 4
## [1] 10 5
## [1] 15 6
## [1] 21 7
## [1] 28 8
## [1] 36 9
## [1] 45 10
## [1] 55 11
## [1] 66 12
## [1] 78 13
## [1] 91 14
## [1] 105 15
## [1] 120 16
## [1] 136 17
## [1] 153 18
## [1] 171 19
## [1] 190 20
## [1] 210 21
## [1] 231 22
## [1] 253 23
## [1] 276 24
## [1] 300 25
## [1] 325 26
## [1] 351 27
## [1] 378 28
## [1] 406 29
## [1] 435 30
## [1] 465 31
## [1] 496 32
## [1] 528 33
## [1] 561 34
## [1] 595 35
## [1] 630 36
## [1] 666 37
## [1] 703 38
## [1] 741 39
## [1] 780 40
## [1] 820 41
## [1] 861 42
## [1] 903 43
## [1] 946 44
## [1] 990 45
## [1] 1035 46
## [1] 1081 47
## [1] 1128 48
## [1] 1176 49
## [1] 1225 50
## [1] 1275 51
## [1] 1326 52
## [1] 1378 53
## [1] 1431 54
## [1] 1485 55
## [1] 1540 56
## [1] 1596 57
## [1] 1653 58
## [1] 1711 59
## [1] 1770 60
## [1] 1830 61
## [1] 1891 62
## [1] 1953 63
## [1] 2016 64
## [1] 2080 65
## [1] 2145 66
## [1] 2211 67
## [1] 2278 68
## [1] 2346 69
## [1] 2415 70
## [1] 2485 71
## [1] 2556 72
## [1] 2628 73
## [1] 2701 74
## [1] 2775 75
## [1] 2850 76
## [1] 2926 77
## [1] 3003 78
## [1] 3081 79
## [1] 3160 80
## [1] 3240 81
## [1] 3321 82
## [1] 3403 83
## [1] 3486 84
## [1] 3570 85
## [1] 3655 86
## [1] 3741 87
## [1] 3828 88
## [1] 3916 89
## [1] 4005 90
## [1] 4095 91
## [1] 4186 92
## [1] 4278 93
## [1] 4371 94
## [1] 4465 95
## [1] 4560 96
## [1] 4656 97
## [1] 4753 98
## [1] 4851 99
## [1] 4950 100
print(sum)
## [1] 5050norow <- nrow(iris) # iris의 행의 수
mylabel <- c() # 비어 있는 벡터 선언
for (i in 1:norow) {
if (iris$Petal.Length[i] <= 1.6) { # 꽃잎의 길이에 따라 레이블 결정
mylabel[i] <- 'L'
} else if (iris$Petal.Length[i] >= 5.1) {
mylabel[i] <- 'H'
} else {
mylabel[i] <- 'M'
}
print(c(iris$Petal.Length[i], mylabel))
}
## [1] "1.4" "L"
## [1] "1.4" "L" "L"
## [1] "1.3" "L" "L" "L"
## [1] "1.5" "L" "L" "L" "L"
## [1] "1.4" "L" "L" "L" "L" "L"
## [1] "1.7" "L" "L" "L" "L" "L" "M"
## [1] "1.4" "L" "L" "L" "L" "L" "M" "L"
## [1] "1.5" "L" "L" "L" "L" "L" "M" "L" "L"
## [1] "1.4" "L" "L" "L" "L" "L" "M" "L" "L" "L"
## [1] "1.5" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L"
## [1] "1.5" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [1] "1.6" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L"
## [1] "1.4" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L"
## [1] "1.1" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L"
## [1] "1.2" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L"
## [1] "1.5" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L"
## [1] "1.3" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L"
## [1] "1.4" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L"
## [1] "1.7" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M"
## [1] "1.5" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L"
## [1] "1.7" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M"
## [1] "1.5" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L"
## [1] "1" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [20] "M" "L" "M" "L" "L"
## [1] "1.7" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M"
## [1] "1.9" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M"
## [1] "1.6" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L"
## [1] "1.6" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L"
## [1] "1.5" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L"
## [1] "1.4" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L"
## [1] "1.6" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L"
## [1] "1.6" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L"
## [1] "1.5" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L"
## [1] "1.5" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L"
## [1] "1.4" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [1] "1.5" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [1] "1.2" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L"
## [1] "1.3" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L"
## [1] "1.4" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L"
## [1] "1.3" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L"
## [1] "1.5" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L"
## [1] "1.3" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L"
## [1] "1.3" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L"
## [1] "1.3" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L"
## [1] "1.6" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [1] "1.9" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M"
## [1] "1.4" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L"
## [1] "1.6" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [1] "1.4" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L"
## [1] "1.5" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L"
## [1] "1.4" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L"
## [1] "4.7" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M"
## [1] "4.5" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M"
## [1] "4.9" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M"
## [1] "4" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [20] "M" "L" "M" "L" "L" "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [39] "L" "L" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L" "M" "M" "M" "M"
## [1] "4.6" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M"
## [1] "4.5" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M"
## [1] "4.7" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M"
## [1] "3.3" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M"
## [1] "4.6" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [1] "3.9" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M"
## [1] "3.5" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M"
## [1] "4.2" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M"
## [1] "4" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [20] "M" "L" "M" "L" "L" "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [39] "L" "L" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L" "M" "M" "M" "M" "M" "M"
## [58] "M" "M" "M" "M" "M" "M" "M"
## [1] "4.7" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M"
## [1] "3.6" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M"
## [1] "4.4" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M" "M"
## [1] "4.5" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M" "M" "M"
## [1] "4.1" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [1] "4.5" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [1] "3.9" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [1] "4.8" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [1] "4" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [20] "M" "L" "M" "L" "L" "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [39] "L" "L" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L" "M" "M" "M" "M" "M" "M"
## [58] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [1] "4.9" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M"
## [1] "4.7" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M" "M"
## [1] "4.3" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M" "M" "M"
## [1] "4.4" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M" "M" "M" "M"
## [1] "4.8" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M" "M" "M" "M" "M"
## [1] "5" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [20] "M" "L" "M" "L" "L" "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [39] "L" "L" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L" "M" "M" "M" "M" "M" "M"
## [58] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [77] "M" "M" "M"
## [1] "4.5" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M" "M" "M" "M" "M" "M" "M"
## [1] "3.5" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [1] "3.8" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [1] "3.7" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [1] "3.9" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [1] "5.1" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [85] "H"
## [1] "4.5" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [85] "H" "M"
## [1] "4.5" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [85] "H" "M" "M"
## [1] "4.7" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [85] "H" "M" "M" "M"
## [1] "4.4" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [85] "H" "M" "M" "M" "M"
## [1] "4.1" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [85] "H" "M" "M" "M" "M" "M"
## [1] "4" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [20] "M" "L" "M" "L" "L" "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [39] "L" "L" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L" "M" "M" "M" "M" "M" "M"
## [58] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [77] "M" "M" "M" "M" "M" "M" "M" "M" "H" "M" "M" "M" "M" "M" "M"
## [1] "4.4" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [85] "H" "M" "M" "M" "M" "M" "M" "M"
## [1] "4.6" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [85] "H" "M" "M" "M" "M" "M" "M" "M" "M"
## [1] "4" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [20] "M" "L" "M" "L" "L" "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [39] "L" "L" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L" "M" "M" "M" "M" "M" "M"
## [58] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [77] "M" "M" "M" "M" "M" "M" "M" "M" "H" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [1] "3.3" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [85] "H" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [1] "4.2" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [85] "H" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [1] "4.2" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [85] "H" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [97] "M"
## [1] "4.2" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [85] "H" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [97] "M" "M"
## [1] "4.3" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [85] "H" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [97] "M" "M" "M"
## [1] "3" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [19] "L" "M" "L" "M" "L" "L" "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L" "M" "M" "M"
## [55] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "H" "M" "M" "M" "M" "M"
## [91] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [1] "4.1" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [85] "H" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [97] "M" "M" "M" "M" "M"
## [1] "6" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [19] "L" "M" "L" "M" "L" "L" "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L" "M" "M" "M"
## [55] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "H" "M" "M" "M" "M" "M"
## [91] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "H"
## [1] "5.1" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [85] "H" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [97] "M" "M" "M" "M" "M" "H" "H"
## [1] "5.9" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [85] "H" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [97] "M" "M" "M" "M" "M" "H" "H" "H"
## [1] "5.6" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [85] "H" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [97] "M" "M" "M" "M" "M" "H" "H" "H" "H"
## [1] "5.8" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [85] "H" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [97] "M" "M" "M" "M" "M" "H" "H" "H" "H" "H"
## [1] "6.6" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [85] "H" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [97] "M" "M" "M" "M" "M" "H" "H" "H" "H" "H" "H"
## [1] "4.5" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [85] "H" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [97] "M" "M" "M" "M" "M" "H" "H" "H" "H" "H" "H" "M"
## [1] "6.3" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [85] "H" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [97] "M" "M" "M" "M" "M" "H" "H" "H" "H" "H" "H" "M"
## [109] "H"
## [1] "5.8" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [85] "H" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [97] "M" "M" "M" "M" "M" "H" "H" "H" "H" "H" "H" "M"
## [109] "H" "H"
## [1] "6.1" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [85] "H" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [97] "M" "M" "M" "M" "M" "H" "H" "H" "H" "H" "H" "M"
## [109] "H" "H" "H"
## [1] "5.1" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [85] "H" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [97] "M" "M" "M" "M" "M" "H" "H" "H" "H" "H" "H" "M"
## [109] "H" "H" "H" "H"
## [1] "5.3" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [85] "H" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [97] "M" "M" "M" "M" "M" "H" "H" "H" "H" "H" "H" "M"
## [109] "H" "H" "H" "H" "H"
## [1] "5.5" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [85] "H" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [97] "M" "M" "M" "M" "M" "H" "H" "H" "H" "H" "H" "M"
## [109] "H" "H" "H" "H" "H" "H"
## [1] "5" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [19] "L" "M" "L" "M" "L" "L" "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L" "M" "M" "M"
## [55] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "H" "M" "M" "M" "M" "M"
## [91] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "H" "H" "H" "H" "H" "H" "M"
## [109] "H" "H" "H" "H" "H" "H" "M"
## [1] "5.1" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [85] "H" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [97] "M" "M" "M" "M" "M" "H" "H" "H" "H" "H" "H" "M"
## [109] "H" "H" "H" "H" "H" "H" "M" "H"
## [1] "5.3" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [85] "H" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [97] "M" "M" "M" "M" "M" "H" "H" "H" "H" "H" "H" "M"
## [109] "H" "H" "H" "H" "H" "H" "M" "H" "H"
## [1] "5.5" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [85] "H" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [97] "M" "M" "M" "M" "M" "H" "H" "H" "H" "H" "H" "M"
## [109] "H" "H" "H" "H" "H" "H" "M" "H" "H" "H"
## [1] "6.7" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [85] "H" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [97] "M" "M" "M" "M" "M" "H" "H" "H" "H" "H" "H" "M"
## [109] "H" "H" "H" "H" "H" "H" "M" "H" "H" "H" "H"
## [1] "6.9" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [85] "H" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [97] "M" "M" "M" "M" "M" "H" "H" "H" "H" "H" "H" "M"
## [109] "H" "H" "H" "H" "H" "H" "M" "H" "H" "H" "H" "H"
## [1] "5" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [19] "L" "M" "L" "M" "L" "L" "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L" "M" "M" "M"
## [55] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "H" "M" "M" "M" "M" "M"
## [91] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "H" "H" "H" "H" "H" "H" "M"
## [109] "H" "H" "H" "H" "H" "H" "M" "H" "H" "H" "H" "H" "M"
## [1] "5.7" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [85] "H" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [97] "M" "M" "M" "M" "M" "H" "H" "H" "H" "H" "H" "M"
## [109] "H" "H" "H" "H" "H" "H" "M" "H" "H" "H" "H" "H"
## [121] "M" "H"
## [1] "4.9" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [85] "H" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [97] "M" "M" "M" "M" "M" "H" "H" "H" "H" "H" "H" "M"
## [109] "H" "H" "H" "H" "H" "H" "M" "H" "H" "H" "H" "H"
## [121] "M" "H" "M"
## [1] "6.7" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [85] "H" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [97] "M" "M" "M" "M" "M" "H" "H" "H" "H" "H" "H" "M"
## [109] "H" "H" "H" "H" "H" "H" "M" "H" "H" "H" "H" "H"
## [121] "M" "H" "M" "H"
## [1] "4.9" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [85] "H" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [97] "M" "M" "M" "M" "M" "H" "H" "H" "H" "H" "H" "M"
## [109] "H" "H" "H" "H" "H" "H" "M" "H" "H" "H" "H" "H"
## [121] "M" "H" "M" "H" "M"
## [1] "5.7" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [85] "H" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [97] "M" "M" "M" "M" "M" "H" "H" "H" "H" "H" "H" "M"
## [109] "H" "H" "H" "H" "H" "H" "M" "H" "H" "H" "H" "H"
## [121] "M" "H" "M" "H" "M" "H"
## [1] "6" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [19] "L" "M" "L" "M" "L" "L" "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L" "M" "M" "M"
## [55] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "H" "M" "M" "M" "M" "M"
## [91] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "H" "H" "H" "H" "H" "H" "M"
## [109] "H" "H" "H" "H" "H" "H" "M" "H" "H" "H" "H" "H" "M" "H" "M" "H" "M" "H"
## [127] "H"
## [1] "4.8" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [85] "H" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [97] "M" "M" "M" "M" "M" "H" "H" "H" "H" "H" "H" "M"
## [109] "H" "H" "H" "H" "H" "H" "M" "H" "H" "H" "H" "H"
## [121] "M" "H" "M" "H" "M" "H" "H" "M"
## [1] "4.9" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [85] "H" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [97] "M" "M" "M" "M" "M" "H" "H" "H" "H" "H" "H" "M"
## [109] "H" "H" "H" "H" "H" "H" "M" "H" "H" "H" "H" "H"
## [121] "M" "H" "M" "H" "M" "H" "H" "M" "M"
## [1] "5.6" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [85] "H" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [97] "M" "M" "M" "M" "M" "H" "H" "H" "H" "H" "H" "M"
## [109] "H" "H" "H" "H" "H" "H" "M" "H" "H" "H" "H" "H"
## [121] "M" "H" "M" "H" "M" "H" "H" "M" "M" "H"
## [1] "5.8" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [85] "H" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [97] "M" "M" "M" "M" "M" "H" "H" "H" "H" "H" "H" "M"
## [109] "H" "H" "H" "H" "H" "H" "M" "H" "H" "H" "H" "H"
## [121] "M" "H" "M" "H" "M" "H" "H" "M" "M" "H" "H"
## [1] "6.1" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [85] "H" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [97] "M" "M" "M" "M" "M" "H" "H" "H" "H" "H" "H" "M"
## [109] "H" "H" "H" "H" "H" "H" "M" "H" "H" "H" "H" "H"
## [121] "M" "H" "M" "H" "M" "H" "H" "M" "M" "H" "H" "H"
## [1] "6.4" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [85] "H" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [97] "M" "M" "M" "M" "M" "H" "H" "H" "H" "H" "H" "M"
## [109] "H" "H" "H" "H" "H" "H" "M" "H" "H" "H" "H" "H"
## [121] "M" "H" "M" "H" "M" "H" "H" "M" "M" "H" "H" "H"
## [133] "H"
## [1] "5.6" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [85] "H" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [97] "M" "M" "M" "M" "M" "H" "H" "H" "H" "H" "H" "M"
## [109] "H" "H" "H" "H" "H" "H" "M" "H" "H" "H" "H" "H"
## [121] "M" "H" "M" "H" "M" "H" "H" "M" "M" "H" "H" "H"
## [133] "H" "H"
## [1] "5.1" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [85] "H" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [97] "M" "M" "M" "M" "M" "H" "H" "H" "H" "H" "H" "M"
## [109] "H" "H" "H" "H" "H" "H" "M" "H" "H" "H" "H" "H"
## [121] "M" "H" "M" "H" "M" "H" "H" "M" "M" "H" "H" "H"
## [133] "H" "H" "H"
## [1] "5.6" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [85] "H" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [97] "M" "M" "M" "M" "M" "H" "H" "H" "H" "H" "H" "M"
## [109] "H" "H" "H" "H" "H" "H" "M" "H" "H" "H" "H" "H"
## [121] "M" "H" "M" "H" "M" "H" "H" "M" "M" "H" "H" "H"
## [133] "H" "H" "H" "H"
## [1] "6.1" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [85] "H" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [97] "M" "M" "M" "M" "M" "H" "H" "H" "H" "H" "H" "M"
## [109] "H" "H" "H" "H" "H" "H" "M" "H" "H" "H" "H" "H"
## [121] "M" "H" "M" "H" "M" "H" "H" "M" "M" "H" "H" "H"
## [133] "H" "H" "H" "H" "H"
## [1] "5.6" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [85] "H" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [97] "M" "M" "M" "M" "M" "H" "H" "H" "H" "H" "H" "M"
## [109] "H" "H" "H" "H" "H" "H" "M" "H" "H" "H" "H" "H"
## [121] "M" "H" "M" "H" "M" "H" "H" "M" "M" "H" "H" "H"
## [133] "H" "H" "H" "H" "H" "H"
## [1] "5.5" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [85] "H" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [97] "M" "M" "M" "M" "M" "H" "H" "H" "H" "H" "H" "M"
## [109] "H" "H" "H" "H" "H" "H" "M" "H" "H" "H" "H" "H"
## [121] "M" "H" "M" "H" "M" "H" "H" "M" "M" "H" "H" "H"
## [133] "H" "H" "H" "H" "H" "H" "H"
## [1] "4.8" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [85] "H" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [97] "M" "M" "M" "M" "M" "H" "H" "H" "H" "H" "H" "M"
## [109] "H" "H" "H" "H" "H" "H" "M" "H" "H" "H" "H" "H"
## [121] "M" "H" "M" "H" "M" "H" "H" "M" "M" "H" "H" "H"
## [133] "H" "H" "H" "H" "H" "H" "H" "M"
## [1] "5.4" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [85] "H" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [97] "M" "M" "M" "M" "M" "H" "H" "H" "H" "H" "H" "M"
## [109] "H" "H" "H" "H" "H" "H" "M" "H" "H" "H" "H" "H"
## [121] "M" "H" "M" "H" "M" "H" "H" "M" "M" "H" "H" "H"
## [133] "H" "H" "H" "H" "H" "H" "H" "M" "H"
## [1] "5.6" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [85] "H" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [97] "M" "M" "M" "M" "M" "H" "H" "H" "H" "H" "H" "M"
## [109] "H" "H" "H" "H" "H" "H" "M" "H" "H" "H" "H" "H"
## [121] "M" "H" "M" "H" "M" "H" "H" "M" "M" "H" "H" "H"
## [133] "H" "H" "H" "H" "H" "H" "H" "M" "H" "H"
## [1] "5.1" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [85] "H" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [97] "M" "M" "M" "M" "M" "H" "H" "H" "H" "H" "H" "M"
## [109] "H" "H" "H" "H" "H" "H" "M" "H" "H" "H" "H" "H"
## [121] "M" "H" "M" "H" "M" "H" "H" "M" "M" "H" "H" "H"
## [133] "H" "H" "H" "H" "H" "H" "H" "M" "H" "H" "H"
## [1] "5.1" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [85] "H" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [97] "M" "M" "M" "M" "M" "H" "H" "H" "H" "H" "H" "M"
## [109] "H" "H" "H" "H" "H" "H" "M" "H" "H" "H" "H" "H"
## [121] "M" "H" "M" "H" "M" "H" "H" "M" "M" "H" "H" "H"
## [133] "H" "H" "H" "H" "H" "H" "H" "M" "H" "H" "H" "H"
## [1] "5.9" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [85] "H" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [97] "M" "M" "M" "M" "M" "H" "H" "H" "H" "H" "H" "M"
## [109] "H" "H" "H" "H" "H" "H" "M" "H" "H" "H" "H" "H"
## [121] "M" "H" "M" "H" "M" "H" "H" "M" "M" "H" "H" "H"
## [133] "H" "H" "H" "H" "H" "H" "H" "M" "H" "H" "H" "H"
## [145] "H"
## [1] "5.7" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [85] "H" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [97] "M" "M" "M" "M" "M" "H" "H" "H" "H" "H" "H" "M"
## [109] "H" "H" "H" "H" "H" "H" "M" "H" "H" "H" "H" "H"
## [121] "M" "H" "M" "H" "M" "H" "H" "M" "M" "H" "H" "H"
## [133] "H" "H" "H" "H" "H" "H" "H" "M" "H" "H" "H" "H"
## [145] "H" "H"
## [1] "5.2" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [85] "H" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [97] "M" "M" "M" "M" "M" "H" "H" "H" "H" "H" "H" "M"
## [109] "H" "H" "H" "H" "H" "H" "M" "H" "H" "H" "H" "H"
## [121] "M" "H" "M" "H" "M" "H" "H" "M" "M" "H" "H" "H"
## [133] "H" "H" "H" "H" "H" "H" "H" "M" "H" "H" "H" "H"
## [145] "H" "H" "H"
## [1] "5" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [19] "L" "M" "L" "M" "L" "L" "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L" "M" "M" "M"
## [55] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "H" "M" "M" "M" "M" "M"
## [91] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "H" "H" "H" "H" "H" "H" "M"
## [109] "H" "H" "H" "H" "H" "H" "M" "H" "H" "H" "H" "H" "M" "H" "M" "H" "M" "H"
## [127] "H" "M" "M" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "M" "H" "H" "H" "H"
## [145] "H" "H" "H" "M"
## [1] "5.2" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [85] "H" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [97] "M" "M" "M" "M" "M" "H" "H" "H" "H" "H" "H" "M"
## [109] "H" "H" "H" "H" "H" "H" "M" "H" "H" "H" "H" "H"
## [121] "M" "H" "M" "H" "M" "H" "H" "M" "M" "H" "H" "H"
## [133] "H" "H" "H" "H" "H" "H" "H" "M" "H" "H" "H" "H"
## [145] "H" "H" "H" "M" "H"
## [1] "5.4" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [85] "H" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [97] "M" "M" "M" "M" "M" "H" "H" "H" "H" "H" "H" "M"
## [109] "H" "H" "H" "H" "H" "H" "M" "H" "H" "H" "H" "H"
## [121] "M" "H" "M" "H" "M" "H" "H" "M" "M" "H" "H" "H"
## [133] "H" "H" "H" "H" "H" "H" "H" "M" "H" "H" "H" "H"
## [145] "H" "H" "H" "M" "H" "H"
## [1] "5.1" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L"
## [13] "L" "L" "L" "L" "L" "L" "L" "M" "L" "M" "L" "L"
## [25] "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L"
## [49] "L" "L" "L" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [61] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [85] "H" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [97] "M" "M" "M" "M" "M" "H" "H" "H" "H" "H" "H" "M"
## [109] "H" "H" "H" "H" "H" "H" "M" "H" "H" "H" "H" "H"
## [121] "M" "H" "M" "H" "M" "H" "H" "M" "M" "H" "H" "H"
## [133] "H" "H" "H" "H" "H" "H" "H" "M" "H" "H" "H" "H"
## [145] "H" "H" "H" "M" "H" "H" "H"
print(mylabel) # 레이블 출력
## [1] "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [19] "M" "L" "M" "L" "L" "M" "M" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L"
## [37] "L" "L" "L" "L" "L" "L" "L" "L" "M" "L" "L" "L" "L" "L" "M" "M" "M" "M"
## [55] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"
## [73] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "H" "M" "M" "M" "M" "M" "M"
## [91] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "H" "H" "H" "H" "H" "H" "M" "H"
## [109] "H" "H" "H" "H" "H" "M" "H" "H" "H" "H" "H" "M" "H" "M" "H" "M" "H" "H"
## [127] "M" "M" "H" "H" "H" "H" "H" "H" "H" "H" "H" "H" "M" "H" "H" "H" "H" "H"
## [145] "H" "H" "M" "H" "H" "H"
newds <- data.frame(iris$Petal.Length, mylabel) # 꽃잎의 길이와 레이블 결합
head(newds) # 새로운 데이터셋 내용 출력
## iris.Petal.Length mylabel
## 1 1.4 L
## 2 1.4 L
## 3 1.3 L
## 4 1.5 L
## 5 1.4 L
## 6 1.7 Msum <- 0
i <- 1
while (i <= 100) {
sum <- sum + i # sum에 i 값을 누적
i <- i + 1 # i 값을 1 증가시킴
print(c(sum, i))
}
## [1] 1 2
## [1] 3 3
## [1] 6 4
## [1] 10 5
## [1] 15 6
## [1] 21 7
## [1] 28 8
## [1] 36 9
## [1] 45 10
## [1] 55 11
## [1] 66 12
## [1] 78 13
## [1] 91 14
## [1] 105 15
## [1] 120 16
## [1] 136 17
## [1] 153 18
## [1] 171 19
## [1] 190 20
## [1] 210 21
## [1] 231 22
## [1] 253 23
## [1] 276 24
## [1] 300 25
## [1] 325 26
## [1] 351 27
## [1] 378 28
## [1] 406 29
## [1] 435 30
## [1] 465 31
## [1] 496 32
## [1] 528 33
## [1] 561 34
## [1] 595 35
## [1] 630 36
## [1] 666 37
## [1] 703 38
## [1] 741 39
## [1] 780 40
## [1] 820 41
## [1] 861 42
## [1] 903 43
## [1] 946 44
## [1] 990 45
## [1] 1035 46
## [1] 1081 47
## [1] 1128 48
## [1] 1176 49
## [1] 1225 50
## [1] 1275 51
## [1] 1326 52
## [1] 1378 53
## [1] 1431 54
## [1] 1485 55
## [1] 1540 56
## [1] 1596 57
## [1] 1653 58
## [1] 1711 59
## [1] 1770 60
## [1] 1830 61
## [1] 1891 62
## [1] 1953 63
## [1] 2016 64
## [1] 2080 65
## [1] 2145 66
## [1] 2211 67
## [1] 2278 68
## [1] 2346 69
## [1] 2415 70
## [1] 2485 71
## [1] 2556 72
## [1] 2628 73
## [1] 2701 74
## [1] 2775 75
## [1] 2850 76
## [1] 2926 77
## [1] 3003 78
## [1] 3081 79
## [1] 3160 80
## [1] 3240 81
## [1] 3321 82
## [1] 3403 83
## [1] 3486 84
## [1] 3570 85
## [1] 3655 86
## [1] 3741 87
## [1] 3828 88
## [1] 3916 89
## [1] 4005 90
## [1] 4095 91
## [1] 4186 92
## [1] 4278 93
## [1] 4371 94
## [1] 4465 95
## [1] 4560 96
## [1] 4656 97
## [1] 4753 98
## [1] 4851 99
## [1] 4950 100
## [1] 5050 101
print(sum)
## [1] 5050
#---------------------------------------#
# 오류 없이 계속 실행됨
# sum <- 0
# i <- 1
# while(i >= 1) {
# sum <- sum + i # sum에 i 값을 누적
# i <- i + 1 # i 값을 1 증가시킴
# print(c(sum,i))
# }
# print(sum)
#---------------------------------------#sum <- 0
for (i in 1:10) {
sum <- sum + i
print(c(sum, i))
if (i >= 5)
break
}
## [1] 1 1
## [1] 3 2
## [1] 6 3
## [1] 10 4
## [1] 15 5
sum
## [1] 15sum <- 0
for (i in 1:10) {
if (i %% 2 == 0)
next # %% = 나머지
sum <- sum + i
print(c(sum, i))
}
## [1] 1 1
## [1] 4 3
## [1] 9 5
## [1] 16 7
## [1] 25 9
sum
## [1] 25apply(iris[, 1:4], 1, mean) # row 방향으로 함수 적용
## [1] 2.550 2.375 2.350 2.350 2.550 2.850 2.425 2.525 2.225 2.400 2.700 2.500
## [13] 2.325 2.125 2.800 3.000 2.750 2.575 2.875 2.675 2.675 2.675 2.350 2.650
## [25] 2.575 2.450 2.600 2.600 2.550 2.425 2.425 2.675 2.725 2.825 2.425 2.400
## [37] 2.625 2.500 2.225 2.550 2.525 2.100 2.275 2.675 2.800 2.375 2.675 2.350
## [49] 2.675 2.475 4.075 3.900 4.100 3.275 3.850 3.575 3.975 2.900 3.850 3.300
## [61] 2.875 3.650 3.300 3.775 3.350 3.900 3.650 3.400 3.600 3.275 3.925 3.550
## [73] 3.800 3.700 3.725 3.850 3.950 4.100 3.725 3.200 3.200 3.150 3.400 3.850
## [85] 3.600 3.875 4.000 3.575 3.500 3.325 3.425 3.775 3.400 2.900 3.450 3.525
## [97] 3.525 3.675 2.925 3.475 4.525 3.875 4.525 4.150 4.375 4.825 3.400 4.575
## [109] 4.200 4.850 4.200 4.075 4.350 3.800 4.025 4.300 4.200 5.100 4.875 3.675
## [121] 4.525 3.825 4.800 3.925 4.450 4.550 3.900 3.950 4.225 4.400 4.550 5.025
## [133] 4.250 3.925 3.925 4.775 4.425 4.200 3.900 4.375 4.450 4.350 3.875 4.550
## [145] 4.550 4.300 3.925 4.175 4.325 3.950
apply(iris[, 1:4], 2, mean) # col 방향으로 함수 적용
## Sepal.Length Sepal.Width Petal.Length Petal.Width
## 5.843333 3.057333 3.758000 1.199333
result <- c()
for (i in 1:4) {
iris_col <- iris[, i]
iris_col_mean_temp <- mean(iris_col)
result <- c(result, iris_col_mean_temp)
}
result
## [1] 5.843333 3.057333 3.758000 1.199333mymax <- function(x, y) {
num.max <- x
if (y > x) {
num.max <- y
}
return(num.max)
}mymax(10, 15)
## [1] 15
a <- mymax(20, 15)
b <- mymax(31, 45)
print(a + b)
## [1] 65mydiv <- function(x, y = 2) {
result <- x / y
return(result)
}
mydiv(x = 10, y = 3) # 매개변수 이름과 매개변수값을 쌍으로 입력
## [1] 3.333333
mydiv(10, 3) # 매개변수값만 입력
## [1] 3.333333
mydiv(10) # x에 대한 값만 입력(y 값이 생략됨)
## [1] 5myfunc <- function(x, y) {
val.sum <- x + y
val.mul <- x * y
return(list(sum = val.sum, mul = val.mul))
}
result <- myfunc(5, 8)
result
## $sum
## [1] 13
##
## $mul
## [1] 40
s <- result$sum # 5, 8의 합
m <- result$mul # 5, 8의 곱
cat('5+8=', s, '\n')
## 5+8= 13
cat('5*8=', m, '\n')
## 5*8= 40getwd()
## [1] "C:/Users/Hyunsoo Kim/Desktop/senior_grade/blog/my-quarto-website"
# source("myfunc.R") # myfunc.R 안에 있는 함수 실행
a <- mydiv(20, 4) # 함수 호출
b <- mydiv(30, 4) # 함수 호출
a + b
## [1] 12.5
mydiv(mydiv(20, 2), 5) # 함수 호출
## [1] 2score <- c(76, 84, 69, 50, 95, 60, 82, 71, 88, 84)
which(score == 69) # 성적이 69인 학생은 몇 번째에 있나
## [1] 3
which(score >= 85) # 성적이 85 이상인 학생은 몇 번째에 있나
## [1] 5 9
max(score) # 최고 점수는 몇 점인가
## [1] 95
which.max(score) # 최고 점수는 몇 번째에 있나
## [1] 5
score[which.max(score)] # 최고 점수는 몇 점인가
## [1] 95
min(score) # 최저 점수는 몇 점인가
## [1] 50
which.min(score) # 최저 점수는 몇 번째에 있나
## [1] 4
score[which.min(score)] # 최저 점수는 몇 점인가
## [1] 50score <- c(76, 84, 69, 50, 95, 60, 82, 71, 88, 84)
idx <- which(score <= 60) # 성적이 60 이하인 값들의 인덱스
idx
## [1] 4 6
score[idx]
## [1] 50 60
score[idx] <- 61 # 성적이 60 이하인 값들은 61점으로 성적 상향 조정
score # 상향 조정된 성적 확인
## [1] 76 84 69 61 95 61 82 71 88 84
idx <- which(score >= 80) # 성적이 80 이상인 값들의 인덱스
idx
## [1] 2 5 7 9 10
score[idx]
## [1] 84 95 82 88 84
score.high <- score[idx] # 성적이 80 이상인 값들만 추출하여 저장
score.high # score.high의 내용 확인
## [1] 84 95 82 88 84iris
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
## 7 4.6 3.4 1.4 0.3 setosa
## 8 5.0 3.4 1.5 0.2 setosa
## 9 4.4 2.9 1.4 0.2 setosa
## 10 4.9 3.1 1.5 0.1 setosa
## 11 5.4 3.7 1.5 0.2 setosa
## 12 4.8 3.4 1.6 0.2 setosa
## 13 4.8 3.0 1.4 0.1 setosa
## 14 4.3 3.0 1.1 0.1 setosa
## 15 5.8 4.0 1.2 0.2 setosa
## 16 5.7 4.4 1.5 0.4 setosa
## 17 5.4 3.9 1.3 0.4 setosa
## 18 5.1 3.5 1.4 0.3 setosa
## 19 5.7 3.8 1.7 0.3 setosa
## 20 5.1 3.8 1.5 0.3 setosa
## 21 5.4 3.4 1.7 0.2 setosa
## 22 5.1 3.7 1.5 0.4 setosa
## 23 4.6 3.6 1.0 0.2 setosa
## 24 5.1 3.3 1.7 0.5 setosa
## 25 4.8 3.4 1.9 0.2 setosa
## 26 5.0 3.0 1.6 0.2 setosa
## 27 5.0 3.4 1.6 0.4 setosa
## 28 5.2 3.5 1.5 0.2 setosa
## 29 5.2 3.4 1.4 0.2 setosa
## 30 4.7 3.2 1.6 0.2 setosa
## 31 4.8 3.1 1.6 0.2 setosa
## 32 5.4 3.4 1.5 0.4 setosa
## 33 5.2 4.1 1.5 0.1 setosa
## 34 5.5 4.2 1.4 0.2 setosa
## 35 4.9 3.1 1.5 0.2 setosa
## 36 5.0 3.2 1.2 0.2 setosa
## 37 5.5 3.5 1.3 0.2 setosa
## 38 4.9 3.6 1.4 0.1 setosa
## 39 4.4 3.0 1.3 0.2 setosa
## 40 5.1 3.4 1.5 0.2 setosa
## 41 5.0 3.5 1.3 0.3 setosa
## 42 4.5 2.3 1.3 0.3 setosa
## 43 4.4 3.2 1.3 0.2 setosa
## 44 5.0 3.5 1.6 0.6 setosa
## 45 5.1 3.8 1.9 0.4 setosa
## 46 4.8 3.0 1.4 0.3 setosa
## 47 5.1 3.8 1.6 0.2 setosa
## 48 4.6 3.2 1.4 0.2 setosa
## 49 5.3 3.7 1.5 0.2 setosa
## 50 5.0 3.3 1.4 0.2 setosa
## 51 7.0 3.2 4.7 1.4 versicolor
## 52 6.4 3.2 4.5 1.5 versicolor
## 53 6.9 3.1 4.9 1.5 versicolor
## 54 5.5 2.3 4.0 1.3 versicolor
## 55 6.5 2.8 4.6 1.5 versicolor
## 56 5.7 2.8 4.5 1.3 versicolor
## 57 6.3 3.3 4.7 1.6 versicolor
## 58 4.9 2.4 3.3 1.0 versicolor
## 59 6.6 2.9 4.6 1.3 versicolor
## 60 5.2 2.7 3.9 1.4 versicolor
## 61 5.0 2.0 3.5 1.0 versicolor
## 62 5.9 3.0 4.2 1.5 versicolor
## 63 6.0 2.2 4.0 1.0 versicolor
## 64 6.1 2.9 4.7 1.4 versicolor
## 65 5.6 2.9 3.6 1.3 versicolor
## 66 6.7 3.1 4.4 1.4 versicolor
## 67 5.6 3.0 4.5 1.5 versicolor
## 68 5.8 2.7 4.1 1.0 versicolor
## 69 6.2 2.2 4.5 1.5 versicolor
## 70 5.6 2.5 3.9 1.1 versicolor
## 71 5.9 3.2 4.8 1.8 versicolor
## 72 6.1 2.8 4.0 1.3 versicolor
## 73 6.3 2.5 4.9 1.5 versicolor
## 74 6.1 2.8 4.7 1.2 versicolor
## 75 6.4 2.9 4.3 1.3 versicolor
## 76 6.6 3.0 4.4 1.4 versicolor
## 77 6.8 2.8 4.8 1.4 versicolor
## 78 6.7 3.0 5.0 1.7 versicolor
## 79 6.0 2.9 4.5 1.5 versicolor
## 80 5.7 2.6 3.5 1.0 versicolor
## 81 5.5 2.4 3.8 1.1 versicolor
## 82 5.5 2.4 3.7 1.0 versicolor
## 83 5.8 2.7 3.9 1.2 versicolor
## 84 6.0 2.7 5.1 1.6 versicolor
## 85 5.4 3.0 4.5 1.5 versicolor
## 86 6.0 3.4 4.5 1.6 versicolor
## 87 6.7 3.1 4.7 1.5 versicolor
## 88 6.3 2.3 4.4 1.3 versicolor
## 89 5.6 3.0 4.1 1.3 versicolor
## 90 5.5 2.5 4.0 1.3 versicolor
## 91 5.5 2.6 4.4 1.2 versicolor
## 92 6.1 3.0 4.6 1.4 versicolor
## 93 5.8 2.6 4.0 1.2 versicolor
## 94 5.0 2.3 3.3 1.0 versicolor
## 95 5.6 2.7 4.2 1.3 versicolor
## 96 5.7 3.0 4.2 1.2 versicolor
## 97 5.7 2.9 4.2 1.3 versicolor
## 98 6.2 2.9 4.3 1.3 versicolor
## 99 5.1 2.5 3.0 1.1 versicolor
## 100 5.7 2.8 4.1 1.3 versicolor
## 101 6.3 3.3 6.0 2.5 virginica
## 102 5.8 2.7 5.1 1.9 virginica
## 103 7.1 3.0 5.9 2.1 virginica
## 104 6.3 2.9 5.6 1.8 virginica
## 105 6.5 3.0 5.8 2.2 virginica
## 106 7.6 3.0 6.6 2.1 virginica
## 107 4.9 2.5 4.5 1.7 virginica
## 108 7.3 2.9 6.3 1.8 virginica
## 109 6.7 2.5 5.8 1.8 virginica
## 110 7.2 3.6 6.1 2.5 virginica
## 111 6.5 3.2 5.1 2.0 virginica
## 112 6.4 2.7 5.3 1.9 virginica
## 113 6.8 3.0 5.5 2.1 virginica
## 114 5.7 2.5 5.0 2.0 virginica
## 115 5.8 2.8 5.1 2.4 virginica
## 116 6.4 3.2 5.3 2.3 virginica
## 117 6.5 3.0 5.5 1.8 virginica
## 118 7.7 3.8 6.7 2.2 virginica
## 119 7.7 2.6 6.9 2.3 virginica
## 120 6.0 2.2 5.0 1.5 virginica
## 121 6.9 3.2 5.7 2.3 virginica
## 122 5.6 2.8 4.9 2.0 virginica
## 123 7.7 2.8 6.7 2.0 virginica
## 124 6.3 2.7 4.9 1.8 virginica
## 125 6.7 3.3 5.7 2.1 virginica
## 126 7.2 3.2 6.0 1.8 virginica
## 127 6.2 2.8 4.8 1.8 virginica
## 128 6.1 3.0 4.9 1.8 virginica
## 129 6.4 2.8 5.6 2.1 virginica
## 130 7.2 3.0 5.8 1.6 virginica
## 131 7.4 2.8 6.1 1.9 virginica
## 132 7.9 3.8 6.4 2.0 virginica
## 133 6.4 2.8 5.6 2.2 virginica
## 134 6.3 2.8 5.1 1.5 virginica
## 135 6.1 2.6 5.6 1.4 virginica
## 136 7.7 3.0 6.1 2.3 virginica
## 137 6.3 3.4 5.6 2.4 virginica
## 138 6.4 3.1 5.5 1.8 virginica
## 139 6.0 3.0 4.8 1.8 virginica
## 140 6.9 3.1 5.4 2.1 virginica
## 141 6.7 3.1 5.6 2.4 virginica
## 142 6.9 3.1 5.1 2.3 virginica
## 143 5.8 2.7 5.1 1.9 virginica
## 144 6.8 3.2 5.9 2.3 virginica
## 145 6.7 3.3 5.7 2.5 virginica
## 146 6.7 3.0 5.2 2.3 virginica
## 147 6.3 2.5 5.0 1.9 virginica
## 148 6.5 3.0 5.2 2.0 virginica
## 149 6.2 3.4 5.4 2.3 virginica
## 150 5.9 3.0 5.1 1.8 virginica
iris$Petal.Length
## [1] 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 1.5 1.6 1.4 1.1 1.2 1.5 1.3 1.4
## [19] 1.7 1.5 1.7 1.5 1.0 1.7 1.9 1.6 1.6 1.5 1.4 1.6 1.6 1.5 1.5 1.4 1.5 1.2
## [37] 1.3 1.4 1.3 1.5 1.3 1.3 1.3 1.6 1.9 1.4 1.6 1.4 1.5 1.4 4.7 4.5 4.9 4.0
## [55] 4.6 4.5 4.7 3.3 4.6 3.9 3.5 4.2 4.0 4.7 3.6 4.4 4.5 4.1 4.5 3.9 4.8 4.0
## [73] 4.9 4.7 4.3 4.4 4.8 5.0 4.5 3.5 3.8 3.7 3.9 5.1 4.5 4.5 4.7 4.4 4.1 4.0
## [91] 4.4 4.6 4.0 3.3 4.2 4.2 4.2 4.3 3.0 4.1 6.0 5.1 5.9 5.6 5.8 6.6 4.5 6.3
## [109] 5.8 6.1 5.1 5.3 5.5 5.0 5.1 5.3 5.5 6.7 6.9 5.0 5.7 4.9 6.7 4.9 5.7 6.0
## [127] 4.8 4.9 5.6 5.8 6.1 6.4 5.6 5.1 5.6 6.1 5.6 5.5 4.8 5.4 5.6 5.1 5.1 5.9
## [145] 5.7 5.2 5.0 5.2 5.4 5.1
iris$Petal.Length > 5.0
## [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [49] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [61] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [73] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
## [85] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [97] FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE
## [109] TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE FALSE
## [121] TRUE FALSE TRUE FALSE TRUE TRUE FALSE FALSE TRUE TRUE TRUE TRUE
## [133] TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE
## [145] TRUE TRUE FALSE TRUE TRUE TRUE
which(iris$Petal.Length > 5.0)
## [1] 84 101 102 103 104 105 106 108 109 110 111 112 113 115 116 117 118 119 121
## [20] 123 125 126 129 130 131 132 133 134 135 136 137 138 140 141 142 143 144 145
## [39] 146 148 149 150
iris$Petal.Length[iris$Petal.Length > 5.0]
## [1] 5.1 6.0 5.1 5.9 5.6 5.8 6.6 6.3 5.8 6.1 5.1 5.3 5.5 5.1 5.3 5.5 6.7 6.9 5.7
## [20] 6.7 5.7 6.0 5.6 5.8 6.1 6.4 5.6 5.1 5.6 6.1 5.6 5.5 5.4 5.6 5.1 5.1 5.9 5.7
## [39] 5.2 5.2 5.4 5.1
idx <- which(iris$Petal.Length > 5.0) # 꽃잎의 길이가 5.0 이상인 값들의 인덱스
idx
## [1] 84 101 102 103 104 105 106 108 109 110 111 112 113 115 116 117 118 119 121
## [20] 123 125 126 129 130 131 132 133 134 135 136 137 138 140 141 142 143 144 145
## [39] 146 148 149 150
iris.big <- iris[idx, ] # 인덱스에 해당하는 값만 추출하여 저장
iris.big
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 84 6.0 2.7 5.1 1.6 versicolor
## 101 6.3 3.3 6.0 2.5 virginica
## 102 5.8 2.7 5.1 1.9 virginica
## 103 7.1 3.0 5.9 2.1 virginica
## 104 6.3 2.9 5.6 1.8 virginica
## 105 6.5 3.0 5.8 2.2 virginica
## 106 7.6 3.0 6.6 2.1 virginica
## 108 7.3 2.9 6.3 1.8 virginica
## 109 6.7 2.5 5.8 1.8 virginica
## 110 7.2 3.6 6.1 2.5 virginica
## 111 6.5 3.2 5.1 2.0 virginica
## 112 6.4 2.7 5.3 1.9 virginica
## 113 6.8 3.0 5.5 2.1 virginica
## 115 5.8 2.8 5.1 2.4 virginica
## 116 6.4 3.2 5.3 2.3 virginica
## 117 6.5 3.0 5.5 1.8 virginica
## 118 7.7 3.8 6.7 2.2 virginica
## 119 7.7 2.6 6.9 2.3 virginica
## 121 6.9 3.2 5.7 2.3 virginica
## 123 7.7 2.8 6.7 2.0 virginica
## 125 6.7 3.3 5.7 2.1 virginica
## 126 7.2 3.2 6.0 1.8 virginica
## 129 6.4 2.8 5.6 2.1 virginica
## 130 7.2 3.0 5.8 1.6 virginica
## 131 7.4 2.8 6.1 1.9 virginica
## 132 7.9 3.8 6.4 2.0 virginica
## 133 6.4 2.8 5.6 2.2 virginica
## 134 6.3 2.8 5.1 1.5 virginica
## 135 6.1 2.6 5.6 1.4 virginica
## 136 7.7 3.0 6.1 2.3 virginica
## 137 6.3 3.4 5.6 2.4 virginica
## 138 6.4 3.1 5.5 1.8 virginica
## 140 6.9 3.1 5.4 2.1 virginica
## 141 6.7 3.1 5.6 2.4 virginica
## 142 6.9 3.1 5.1 2.3 virginica
## 143 5.8 2.7 5.1 1.9 virginica
## 144 6.8 3.2 5.9 2.3 virginica
## 145 6.7 3.3 5.7 2.5 virginica
## 146 6.7 3.0 5.2 2.3 virginica
## 148 6.5 3.0 5.2 2.0 virginica
## 149 6.2 3.4 5.4 2.3 virginica
## 150 5.9 3.0 5.1 1.8 virginica# 1~4열의 값 중 5보다 큰 값의 행과 열의 위치
which(iris[, 1:4] > 5.0)
## [1] 1 6 11 15 16 17 18 19 20 21 22 24 28 29 32 33 34 37
## [19] 40 45 47 49 51 52 53 54 55 56 57 59 60 62 63 64 65 66
## [37] 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84
## [55] 85 86 87 88 89 90 91 92 93 95 96 97 98 99 100 101 102 103
## [73] 104 105 106 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122
## [91] 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140
## [109] 141 142 143 144 145 146 147 148 149 150 384 401 402 403 404 405 406 408
## [127] 409 410 411 412 413 415 416 417 418 419 421 423 425 426 429 430 431 432
## [145] 433 434 435 436 437 438 440 441 442 443 444 445 446 448 449 450
which(iris[, 1:4] > 5.0, arr.ind = TRUE) # arr.ind = TRUE : 조건에 맞는 인덱스까지 반환
## row col
## [1,] 1 1
## [2,] 6 1
## [3,] 11 1
## [4,] 15 1
## [5,] 16 1
## [6,] 17 1
## [7,] 18 1
## [8,] 19 1
## [9,] 20 1
## [10,] 21 1
## [11,] 22 1
## [12,] 24 1
## [13,] 28 1
## [14,] 29 1
## [15,] 32 1
## [16,] 33 1
## [17,] 34 1
## [18,] 37 1
## [19,] 40 1
## [20,] 45 1
## [21,] 47 1
## [22,] 49 1
## [23,] 51 1
## [24,] 52 1
## [25,] 53 1
## [26,] 54 1
## [27,] 55 1
## [28,] 56 1
## [29,] 57 1
## [30,] 59 1
## [31,] 60 1
## [32,] 62 1
## [33,] 63 1
## [34,] 64 1
## [35,] 65 1
## [36,] 66 1
## [37,] 67 1
## [38,] 68 1
## [39,] 69 1
## [40,] 70 1
## [41,] 71 1
## [42,] 72 1
## [43,] 73 1
## [44,] 74 1
## [45,] 75 1
## [46,] 76 1
## [47,] 77 1
## [48,] 78 1
## [49,] 79 1
## [50,] 80 1
## [51,] 81 1
## [52,] 82 1
## [53,] 83 1
## [54,] 84 1
## [55,] 85 1
## [56,] 86 1
## [57,] 87 1
## [58,] 88 1
## [59,] 89 1
## [60,] 90 1
## [61,] 91 1
## [62,] 92 1
## [63,] 93 1
## [64,] 95 1
## [65,] 96 1
## [66,] 97 1
## [67,] 98 1
## [68,] 99 1
## [69,] 100 1
## [70,] 101 1
## [71,] 102 1
## [72,] 103 1
## [73,] 104 1
## [74,] 105 1
## [75,] 106 1
## [76,] 108 1
## [77,] 109 1
## [78,] 110 1
## [79,] 111 1
## [80,] 112 1
## [81,] 113 1
## [82,] 114 1
## [83,] 115 1
## [84,] 116 1
## [85,] 117 1
## [86,] 118 1
## [87,] 119 1
## [88,] 120 1
## [89,] 121 1
## [90,] 122 1
## [91,] 123 1
## [92,] 124 1
## [93,] 125 1
## [94,] 126 1
## [95,] 127 1
## [96,] 128 1
## [97,] 129 1
## [98,] 130 1
## [99,] 131 1
## [100,] 132 1
## [101,] 133 1
## [102,] 134 1
## [103,] 135 1
## [104,] 136 1
## [105,] 137 1
## [106,] 138 1
## [107,] 139 1
## [108,] 140 1
## [109,] 141 1
## [110,] 142 1
## [111,] 143 1
## [112,] 144 1
## [113,] 145 1
## [114,] 146 1
## [115,] 147 1
## [116,] 148 1
## [117,] 149 1
## [118,] 150 1
## [119,] 84 3
## [120,] 101 3
## [121,] 102 3
## [122,] 103 3
## [123,] 104 3
## [124,] 105 3
## [125,] 106 3
## [126,] 108 3
## [127,] 109 3
## [128,] 110 3
## [129,] 111 3
## [130,] 112 3
## [131,] 113 3
## [132,] 115 3
## [133,] 116 3
## [134,] 117 3
## [135,] 118 3
## [136,] 119 3
## [137,] 121 3
## [138,] 123 3
## [139,] 125 3
## [140,] 126 3
## [141,] 129 3
## [142,] 130 3
## [143,] 131 3
## [144,] 132 3
## [145,] 133 3
## [146,] 134 3
## [147,] 135 3
## [148,] 136 3
## [149,] 137 3
## [150,] 138 3
## [151,] 140 3
## [152,] 141 3
## [153,] 142 3
## [154,] 143 3
## [155,] 144 3
## [156,] 145 3
## [157,] 146 3
## [158,] 148 3
## [159,] 149 3
## [160,] 150 3
idx <- which(iris[, 1:4] > 5.0, arr.ind = TRUE)
iris[idx[, 1], ]
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
## 11 5.4 3.7 1.5 0.2 setosa
## 15 5.8 4.0 1.2 0.2 setosa
## 16 5.7 4.4 1.5 0.4 setosa
## 17 5.4 3.9 1.3 0.4 setosa
## 18 5.1 3.5 1.4 0.3 setosa
## 19 5.7 3.8 1.7 0.3 setosa
## 20 5.1 3.8 1.5 0.3 setosa
## 21 5.4 3.4 1.7 0.2 setosa
## 22 5.1 3.7 1.5 0.4 setosa
## 24 5.1 3.3 1.7 0.5 setosa
## 28 5.2 3.5 1.5 0.2 setosa
## 29 5.2 3.4 1.4 0.2 setosa
## 32 5.4 3.4 1.5 0.4 setosa
## 33 5.2 4.1 1.5 0.1 setosa
## 34 5.5 4.2 1.4 0.2 setosa
## 37 5.5 3.5 1.3 0.2 setosa
## 40 5.1 3.4 1.5 0.2 setosa
## 45 5.1 3.8 1.9 0.4 setosa
## 47 5.1 3.8 1.6 0.2 setosa
## 49 5.3 3.7 1.5 0.2 setosa
## 51 7.0 3.2 4.7 1.4 versicolor
## 52 6.4 3.2 4.5 1.5 versicolor
## 53 6.9 3.1 4.9 1.5 versicolor
## 54 5.5 2.3 4.0 1.3 versicolor
## 55 6.5 2.8 4.6 1.5 versicolor
## 56 5.7 2.8 4.5 1.3 versicolor
## 57 6.3 3.3 4.7 1.6 versicolor
## 59 6.6 2.9 4.6 1.3 versicolor
## 60 5.2 2.7 3.9 1.4 versicolor
## 62 5.9 3.0 4.2 1.5 versicolor
## 63 6.0 2.2 4.0 1.0 versicolor
## 64 6.1 2.9 4.7 1.4 versicolor
## 65 5.6 2.9 3.6 1.3 versicolor
## 66 6.7 3.1 4.4 1.4 versicolor
## 67 5.6 3.0 4.5 1.5 versicolor
## 68 5.8 2.7 4.1 1.0 versicolor
## 69 6.2 2.2 4.5 1.5 versicolor
## 70 5.6 2.5 3.9 1.1 versicolor
## 71 5.9 3.2 4.8 1.8 versicolor
## 72 6.1 2.8 4.0 1.3 versicolor
## 73 6.3 2.5 4.9 1.5 versicolor
## 74 6.1 2.8 4.7 1.2 versicolor
## 75 6.4 2.9 4.3 1.3 versicolor
## 76 6.6 3.0 4.4 1.4 versicolor
## 77 6.8 2.8 4.8 1.4 versicolor
## 78 6.7 3.0 5.0 1.7 versicolor
## 79 6.0 2.9 4.5 1.5 versicolor
## 80 5.7 2.6 3.5 1.0 versicolor
## 81 5.5 2.4 3.8 1.1 versicolor
## 82 5.5 2.4 3.7 1.0 versicolor
## 83 5.8 2.7 3.9 1.2 versicolor
## 84 6.0 2.7 5.1 1.6 versicolor
## 85 5.4 3.0 4.5 1.5 versicolor
## 86 6.0 3.4 4.5 1.6 versicolor
## 87 6.7 3.1 4.7 1.5 versicolor
## 88 6.3 2.3 4.4 1.3 versicolor
## 89 5.6 3.0 4.1 1.3 versicolor
## 90 5.5 2.5 4.0 1.3 versicolor
## 91 5.5 2.6 4.4 1.2 versicolor
## 92 6.1 3.0 4.6 1.4 versicolor
## 93 5.8 2.6 4.0 1.2 versicolor
## 95 5.6 2.7 4.2 1.3 versicolor
## 96 5.7 3.0 4.2 1.2 versicolor
## 97 5.7 2.9 4.2 1.3 versicolor
## 98 6.2 2.9 4.3 1.3 versicolor
## 99 5.1 2.5 3.0 1.1 versicolor
## 100 5.7 2.8 4.1 1.3 versicolor
## 101 6.3 3.3 6.0 2.5 virginica
## 102 5.8 2.7 5.1 1.9 virginica
## 103 7.1 3.0 5.9 2.1 virginica
## 104 6.3 2.9 5.6 1.8 virginica
## 105 6.5 3.0 5.8 2.2 virginica
## 106 7.6 3.0 6.6 2.1 virginica
## 108 7.3 2.9 6.3 1.8 virginica
## 109 6.7 2.5 5.8 1.8 virginica
## 110 7.2 3.6 6.1 2.5 virginica
## 111 6.5 3.2 5.1 2.0 virginica
## 112 6.4 2.7 5.3 1.9 virginica
## 113 6.8 3.0 5.5 2.1 virginica
## 114 5.7 2.5 5.0 2.0 virginica
## 115 5.8 2.8 5.1 2.4 virginica
## 116 6.4 3.2 5.3 2.3 virginica
## 117 6.5 3.0 5.5 1.8 virginica
## 118 7.7 3.8 6.7 2.2 virginica
## 119 7.7 2.6 6.9 2.3 virginica
## 120 6.0 2.2 5.0 1.5 virginica
## 121 6.9 3.2 5.7 2.3 virginica
## 122 5.6 2.8 4.9 2.0 virginica
## 123 7.7 2.8 6.7 2.0 virginica
## 124 6.3 2.7 4.9 1.8 virginica
## 125 6.7 3.3 5.7 2.1 virginica
## 126 7.2 3.2 6.0 1.8 virginica
## 127 6.2 2.8 4.8 1.8 virginica
## 128 6.1 3.0 4.9 1.8 virginica
## 129 6.4 2.8 5.6 2.1 virginica
## 130 7.2 3.0 5.8 1.6 virginica
## 131 7.4 2.8 6.1 1.9 virginica
## 132 7.9 3.8 6.4 2.0 virginica
## 133 6.4 2.8 5.6 2.2 virginica
## 134 6.3 2.8 5.1 1.5 virginica
## 135 6.1 2.6 5.6 1.4 virginica
## 136 7.7 3.0 6.1 2.3 virginica
## 137 6.3 3.4 5.6 2.4 virginica
## 138 6.4 3.1 5.5 1.8 virginica
## 139 6.0 3.0 4.8 1.8 virginica
## 140 6.9 3.1 5.4 2.1 virginica
## 141 6.7 3.1 5.6 2.4 virginica
## 142 6.9 3.1 5.1 2.3 virginica
## 143 5.8 2.7 5.1 1.9 virginica
## 144 6.8 3.2 5.9 2.3 virginica
## 145 6.7 3.3 5.7 2.5 virginica
## 146 6.7 3.0 5.2 2.3 virginica
## 147 6.3 2.5 5.0 1.9 virginica
## 148 6.5 3.0 5.2 2.0 virginica
## 149 6.2 3.4 5.4 2.3 virginica
## 150 5.9 3.0 5.1 1.8 virginica
## 84.1 6.0 2.7 5.1 1.6 versicolor
## 101.1 6.3 3.3 6.0 2.5 virginica
## 102.1 5.8 2.7 5.1 1.9 virginica
## 103.1 7.1 3.0 5.9 2.1 virginica
## 104.1 6.3 2.9 5.6 1.8 virginica
## 105.1 6.5 3.0 5.8 2.2 virginica
## 106.1 7.6 3.0 6.6 2.1 virginica
## 108.1 7.3 2.9 6.3 1.8 virginica
## 109.1 6.7 2.5 5.8 1.8 virginica
## 110.1 7.2 3.6 6.1 2.5 virginica
## 111.1 6.5 3.2 5.1 2.0 virginica
## 112.1 6.4 2.7 5.3 1.9 virginica
## 113.1 6.8 3.0 5.5 2.1 virginica
## 115.1 5.8 2.8 5.1 2.4 virginica
## 116.1 6.4 3.2 5.3 2.3 virginica
## 117.1 6.5 3.0 5.5 1.8 virginica
## 118.1 7.7 3.8 6.7 2.2 virginica
## 119.1 7.7 2.6 6.9 2.3 virginica
## 121.1 6.9 3.2 5.7 2.3 virginica
## 123.1 7.7 2.8 6.7 2.0 virginica
## 125.1 6.7 3.3 5.7 2.1 virginica
## 126.1 7.2 3.2 6.0 1.8 virginica
## 129.1 6.4 2.8 5.6 2.1 virginica
## 130.1 7.2 3.0 5.8 1.6 virginica
## 131.1 7.4 2.8 6.1 1.9 virginica
## 132.1 7.9 3.8 6.4 2.0 virginica
## 133.1 6.4 2.8 5.6 2.2 virginica
## 134.1 6.3 2.8 5.1 1.5 virginica
## 135.1 6.1 2.6 5.6 1.4 virginica
## 136.1 7.7 3.0 6.1 2.3 virginica
## 137.1 6.3 3.4 5.6 2.4 virginica
## 138.1 6.4 3.1 5.5 1.8 virginica
## 140.1 6.9 3.1 5.4 2.1 virginica
## 141.1 6.7 3.1 5.6 2.4 virginica
## 142.1 6.9 3.1 5.1 2.3 virginica
## 143.1 5.8 2.7 5.1 1.9 virginica
## 144.1 6.8 3.2 5.9 2.3 virginica
## 145.1 6.7 3.3 5.7 2.5 virginica
## 146.1 6.7 3.0 5.2 2.3 virginica
## 148.1 6.5 3.0 5.2 2.0 virginica
## 149.1 6.2 3.4 5.4 2.3 virginica
## 150.1 5.9 3.0 5.1 1.8 virginica
iris[, 1:4][idx]
## [1] 5.1 5.4 5.4 5.8 5.7 5.4 5.1 5.7 5.1 5.4 5.1 5.1 5.2 5.2 5.4 5.2 5.5 5.5
## [19] 5.1 5.1 5.1 5.3 7.0 6.4 6.9 5.5 6.5 5.7 6.3 6.6 5.2 5.9 6.0 6.1 5.6 6.7
## [37] 5.6 5.8 6.2 5.6 5.9 6.1 6.3 6.1 6.4 6.6 6.8 6.7 6.0 5.7 5.5 5.5 5.8 6.0
## [55] 5.4 6.0 6.7 6.3 5.6 5.5 5.5 6.1 5.8 5.6 5.7 5.7 6.2 5.1 5.7 6.3 5.8 7.1
## [73] 6.3 6.5 7.6 7.3 6.7 7.2 6.5 6.4 6.8 5.7 5.8 6.4 6.5 7.7 7.7 6.0 6.9 5.6
## [91] 7.7 6.3 6.7 7.2 6.2 6.1 6.4 7.2 7.4 7.9 6.4 6.3 6.1 7.7 6.3 6.4 6.0 6.9
## [109] 6.7 6.9 5.8 6.8 6.7 6.7 6.3 6.5 6.2 5.9 5.1 6.0 5.1 5.9 5.6 5.8 6.6 6.3
## [127] 5.8 6.1 5.1 5.3 5.5 5.1 5.3 5.5 6.7 6.9 5.7 6.7 5.7 6.0 5.6 5.8 6.1 6.4
## [145] 5.6 5.1 5.6 6.1 5.6 5.5 5.4 5.6 5.1 5.1 5.9 5.7 5.2 5.2 5.4 5.15장. 단일변수 자료의 탐색
favorite <- c('WINTER', 'SUMMER', 'SPRING', 'SUMMER', 'SUMMER',
'FALL', 'FALL', 'SUMMER', 'SPRING', 'SPRING')
favorite # favorite의 내용 출력
## [1] "WINTER" "SUMMER" "SPRING" "SUMMER" "SUMMER" "FALL" "FALL" "SUMMER"
## [9] "SPRING" "SPRING"
table(favorite) # 도수분포표 계산
## favorite
## FALL SPRING SUMMER WINTER
## 2 3 4 1
length(favorite)
## [1] 10
table(favorite) / length(favorite) # 비율 출력
## favorite
## FALL SPRING SUMMER WINTER
## 0.2 0.3 0.4 0.1ds <- table(favorite)
ds
## favorite
## FALL SPRING SUMMER WINTER
## 2 3 4 1
barplot(ds, main = 'favorite season')
ds <- table(favorite)
ds
## favorite
## FALL SPRING SUMMER WINTER
## 2 3 4 1
pie(ds, main = 'favorite season')
favorite.color <- c(2, 3, 2, 1, 1, 2, 2, 1, 3, 2, 1, 3, 2, 1, 2)
ds <- table(favorite.color)
ds
## favorite.color
## 1 2 3
## 5 7 3
barplot(ds, main = 'favorite color')
colors <- c('green', 'red', 'blue')
names(ds) <- colors # 자료값 1, 2, 3을 green, red, blue로 변경
ds
## green red blue
## 5 7 3
barplot(ds, main = 'favorite color', col = colors) # 색 지정 막대그래프
barplot(ds, main = 'favorite color', col = c('green', 'red', 'blue'))
pie(ds, main = 'favorite color', col = colors) # 색 지정 원그래프
weight <- c(60, 62, 64, 65, 68, 69)
weight.heavy <- c(weight, 120)
weight
## [1] 60 62 64 65 68 69
weight.heavy
## [1] 60 62 64 65 68 69 120
mean(weight) # 평균
## [1] 64.66667
mean(weight.heavy) # 평균
## [1] 72.57143
median(weight) # 중앙값
## [1] 64.5
median(weight.heavy) # 중앙값
## [1] 65
mean(weight, trim = 0.2) # 절사평균(상하위 20% 제외)
## [1] 64.75
mean(weight.heavy, trim = 0.2) # 절사평균(상하위 20% 제외)
## [1] 65.6mydata <- c(60, 62, 64, 65, 68, 69, 120)
quantile(mydata)
## 0% 25% 50% 75% 100%
## 60.0 63.0 65.0 68.5 120.0
quantile(mydata, (0:10) / 10) # 10% 단위로 구간을 나누어 계산
## 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
## 60.0 61.2 62.4 63.6 64.4 65.0 66.8 68.2 68.8 89.4 120.0
summary(mydata) # 최소값, 중앙값, 평균값, 3분위 값, 최대값
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 60.00 63.00 65.00 72.57 68.50 120.00
mydata <- 0:1000
quantile(mydata)
## 0% 25% 50% 75% 100%
## 0 250 500 750 1000
quantile(mydata, (0:10) / 10)
## 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
## 0 100 200 300 400 500 600 700 800 900 1000
summary(mydata)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 250 500 500 750 1000
?quantile
## starting httpd help server ... donemydata <- c(60, 62, 64, 65, 68, 69, 120)
var(mydata) # 분산
## [1] 447.2857
sd(mydata) # 표준편차
## [1] 21.14913
range(mydata) # 값의 범위
## [1] 60 120
diff(range(mydata)) # 최대값, 최소값의 차이
## [1] 60dist <- cars[, 2] # 자동차 제동거리
hist(dist, # 자료(data)
main = "Histogram for 제동거리", # 제목
xlab = "제동거리", # x축 레이블
ylab = "빈도수", # y축 레이블
border = "blue", # 막대 테두리색
col = rainbow(10), # 막대 색
las = 2, # x축 글씨 방향(0~3)
breaks = seq(0, 120, 10)) # 막대 개수 조절
dist <- cars[,2] # 자동차 제동거리(단위: 피트(ft))
boxplot(dist, main = "자동차 제동거리") # ★★★★★
boxplot.stats(dist)
## $stats
## [1] 2 26 36 56 93
##
## $n
## [1] 50
##
## $conf
## [1] 29.29663 42.70337
##
## $out
## [1] 120
boxplot.stats(dist)$stats
## [1] 2 26 36 56 93
boxplot.stats(dist)$stats[4]
## [1] 56boxplot(Petal.Length ~ Species, data = iris, main = "품종별 꽃잎의 길이")
par(mfrow = c(1, 3)) # 1*3 가상화면 분할
barplot(
table(mtcars$carb),
main = "Barplot of Carburetors",
xlab = "#of carburetors",
ylab = "frequency",
col = "blue"
)
barplot(
table(mtcars$cyl),
main = "Barplot of Cylender",
xlab = "#of cylender",
ylab = "frequency",
col = "red"
)
barplot(
table(mtcars$gear),
main = "Barplot of Grar",
xlab = "#of gears",
ylab = "frequency",
col = "green"
)
par(mfrow = c(1, 1)) # 가상화면 분할 해제6장. 다중변수 자료의 탐색
wt <- mtcars$wt # 중량 자료
mpg <- mtcars$mpg # 연비 자료
plot(wt, mpg, # 2개 변수(x축, y축)
main = "중량-연비 그래프", # 제목
xlab = "중량", # x축 레이블
ylab = "연비(MPG)", # y축 레이블
col = "red", # point의 color
pch = 11) # point의 종류
vars <- c("mpg", "disp", "drat", "wt") # 대상 변수(연비, 배기량, 후방차측 비율, 중량)
target <- mtcars[, vars]
head(target)
## mpg disp drat wt
## Mazda RX4 21.0 160 3.90 2.620
## Mazda RX4 Wag 21.0 160 3.90 2.875
## Datsun 710 22.8 108 3.85 2.320
## Hornet 4 Drive 21.4 258 3.08 3.215
## Hornet Sportabout 18.7 360 3.15 3.440
## Valiant 18.1 225 2.76 3.460
pairs(target, main = "Multi Plots") # 대상 데이터
iris.2 <- iris[, 3:4] # 데이터 준비
point <- as.numeric(iris$Species) # 점의 모양
point # point 내용 출력
## [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [75] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3
## [112] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [149] 3 3
color <- c("red", "green", "blue") # 점의 컬러
plot(iris.2,
main = "Iris plot",
pch = c(point),
col = color[point])
beers = c(5, 2, 9, 8, 3, 7, 3, 5, 3, 5) # 자료 입력
bal <- c(0.1, 0.03, 0.19, 0.12, 0.04, 0.0095, 0.07, 0.06, 0.02, 0.05)
tbl <- data.frame(beers, bal) # 데이터프레임 생성
tbl
## beers bal
## 1 5 0.1000
## 2 2 0.0300
## 3 9 0.1900
## 4 8 0.1200
## 5 3 0.0400
## 6 7 0.0095
## 7 3 0.0700
## 8 5 0.0600
## 9 3 0.0200
## 10 5 0.0500
plot(bal ~ beers, data = tbl) # 산점도 plot(beers, bal)
res <- lm(bal ~ beers, data = tbl) # 회귀식 도출
abline(res) # 회귀선 그리기
cor(beers, bal) # 상관계수 계산
## [1] 0.6797025cor(iris[, 1:4]) # 4개 변수 간 상관성 분석
## Sepal.Length Sepal.Width Petal.Length Petal.Width
## Sepal.Length 1.0000000 -0.1175698 0.8717538 0.8179411
## Sepal.Width -0.1175698 1.0000000 -0.4284401 -0.3661259
## Petal.Length 0.8717538 -0.4284401 1.0000000 0.9628654
## Petal.Width 0.8179411 -0.3661259 0.9628654 1.0000000month = 1:12 # 자료 입력
late = c(5, 8, 7, 9, 4, 6, 12, 13, 8, 6, 6, 4) # 자료 입력
plot(month, # x data
late, # y data
main = "지각생 통계", # 제목
type = "l", # 그래프의 종류 선택(알파벳)
lty = 1, # 선의 종류(line type) 선택
lwd = 1, # 선의 굵기 선택
xlab = "Month", # x축 레이블
ylab = "Late cnt") # y축 레이블
month = 1:12
late1 = c(5, 8, 7, 9, 4, 6, 12, 13, 8, 6, 6, 4)
late2 = c(4, 6, 5, 8, 7, 8, 10, 11, 6, 5, 7, 3)
plot(month, # x data
late1, # y data
main = "Late Students",
type = "b", # 그래프의 종류 선택(알파벳)
lty = 1, # 선의 종류(line type) 선택
col = "red", # 선의 색 선택
xlab = "Month", # x축 레이블
ylab = "Late cnt", # y축 레이블
ylim = c(1, 15)) # y축 값의 (하한, 상한)
lines(month, # x data
late2, # y data
type = "b", # 선의 종류(line type) 선택
col = "blue") # 선의 색 선택
## (1) 분석 대상 데이터셋 준비
# install.packages("mlbench")
library(mlbench)
data("BostonHousing")
myds <- BostonHousing[, c("crim", "rm", "dis", "tax", "medv")]
## (2) grp 변수 추가 ★★★★★
grp <- c()
for (i in 1:nrow(myds)) {
# myds$medv 값에 따라 그룹 분류
if (myds$medv[i] >= 25.0) {
grp[i] <- "H"
} else if (myds$medv[i] <= 17.0) {
grp[i] <- "L"
} else {
grp[i] <- "M"
}
}
grp <- factor(grp) # 문자 벡터를 팩터 타입으로 변경
grp <- factor(grp, levels = c("H", "M", "L")) # 레벨의 순서를 H, L, M -> H, M, L
myds <- data.frame(myds, grp) # myds에 grp 열 추가
## (3) 데이터셋의 형태와 기본적인 내용 파악
str(myds)
## 'data.frame': 506 obs. of 6 variables:
## $ crim: num 0.00632 0.02731 0.02729 0.03237 0.06905 ...
## $ rm : num 6.58 6.42 7.18 7 7.15 ...
## $ dis : num 4.09 4.97 4.97 6.06 6.06 ...
## $ tax : num 296 242 242 222 222 222 311 311 311 311 ...
## $ medv: num 24 21.6 34.7 33.4 36.2 28.7 22.9 27.1 16.5 18.9 ...
## $ grp : Factor w/ 3 levels "H","M","L": 2 2 1 1 1 1 2 1 3 2 ...
head(myds)
## crim rm dis tax medv grp
## 1 0.00632 6.575 4.0900 296 24.0 M
## 2 0.02731 6.421 4.9671 242 21.6 M
## 3 0.02729 7.185 4.9671 242 34.7 H
## 4 0.03237 6.998 6.0622 222 33.4 H
## 5 0.06905 7.147 6.0622 222 36.2 H
## 6 0.02985 6.430 6.0622 222 28.7 H
table(myds$grp) # 주택 가격 그룹별 분포
##
## H M L
## 132 247 127
## (4) 히스토그램에 의한 관측값의 분포 확인
par(mfrow = c(2, 3)) # 2*3 가상화면 분할
for (i in 1:5) {
hist(myds[, i], main = colnames(myds)[i], col = "yellow")
}
par(mfrow = c(1, 1)) # 2*3 가상화면 분할 해제
## (5) 상자그림에 의한 관측값의 분포 확인
par(mfrow = c(2, 3)) # 2*3 가상화면 분할
for (i in 1:5) {
boxplot(myds[, i], main = colnames(myds)[i])
}
par(mfrow = c(1, 1)) # 2*3 가상화면 분할 해제
## (6) 그룹별 관측값 분포의 확인
boxplot(myds$crim ~ myds$grp, main = "1인당 범죄율")
boxplot(myds$rm ~ myds$grp, main = "방의 개수")
boxplot(myds$dis ~ myds$grp, main = "직업 센터까지의 거리")
boxplot(myds$tax ~ myds$grp, main = "재산세율")
## (7) 다중 산점도를 통한 변수 간 상관 관계의 확인
pairs(myds[, -6]) # 6번째 열 제거(grp)
pairs(myds[, 1:5])
## (8) 그룹 정보를 포함한 변수 간 상관 관계의 확인
point <- as.integer(myds$grp) # 점의 모양 지정
color <- c("red", "green", "blue") # 점의 색 지정
pairs(myds[, -6], pch = point, col = color[point])
## (9) 변수 간 상관계수의 확인
cor(myds[, -6])
## crim rm dis tax medv
## crim 1.0000000 -0.2192467 -0.3796701 0.5827643 -0.3883046
## rm -0.2192467 1.0000000 0.2052462 -0.2920478 0.6953599
## dis -0.3796701 0.2052462 1.0000000 -0.5344316 0.2499287
## tax 0.5827643 -0.2920478 -0.5344316 1.0000000 -0.4685359
## medv -0.3883046 0.6953599 0.2499287 -0.4685359 1.0000000
cor(myds[1:5])
## crim rm dis tax medv
## crim 1.0000000 -0.2192467 -0.3796701 0.5827643 -0.3883046
## rm -0.2192467 1.0000000 0.2052462 -0.2920478 0.6953599
## dis -0.3796701 0.2052462 1.0000000 -0.5344316 0.2499287
## tax 0.5827643 -0.2920478 -0.5344316 1.0000000 -0.4685359
## medv -0.3883046 0.6953599 0.2499287 -0.4685359 1.00000007장. 데이터 전처리
z <- c(1, 2, 3, NA, 5, NA, 8) # 결측값이 포함된 벡터 z
sum(z) # 정상 계산이 안 됨
## [1] NA
is.na(z) # NA 여부 확인
## [1] FALSE FALSE FALSE TRUE FALSE TRUE FALSE
sum(is.na(z)) # NA의 개수 확인
## [1] 2
sum(z, na.rm = TRUE) # NA를 제외하고 합계를 계산
## [1] 19z1 <- c(1, 2, 3, NA, 5, NA, 8) # 결측값이 포함된 벡터 z1
z2 <- c(5, 8, 1, NA, 3, NA, 7) # 결측값이 포함된 벡터 z2
z1[is.na(z1)] <- 0 # NA를 0으로 치환
z1
## [1] 1 2 3 0 5 0 8
z1[is.na(z1)] <- mean(z1, na.rm = TRUE) # NA를 z1의 평균값으로 치환
z1
## [1] 1 2 3 0 5 0 8
z3 <- as.vector(na.omit(z2)) # NA를 제거하고 새로운 벡터 생성
z3
## [1] 5 8 1 3 7# NA를 포함하는 test 데이터 생성
x <- iris
x[1, 2] <- NA
x[1, 3] <- NA
x[2, 3] <- NA
x[3, 4] <- NA
head(x)
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 NA NA 0.2 setosa
## 2 4.9 3.0 NA 0.2 setosa
## 3 4.7 3.2 1.3 NA setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa# for문을 이용한 방법 ★★★★★
for (i in 1:ncol(x)) {
this.na <- is.na(x[, i])
cat(colnames(x)[i], "\t", sum(this.na), "\n")
}
## Sepal.Length 0
## Sepal.Width 1
## Petal.Length 2
## Petal.Width 1
## Species 0
# apply를 이용한 방법
col_na <- function(y) {
return(sum(is.na(y)))
}
na_count <- apply(x, 2, FUN = col_na)
na_count
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 0 1 2 1 0rowSums(is.na(x)) # 행별 NA의 개수
## [1] 2 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [38] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [75] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [112] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [149] 0 0
sum(rowSums(is.na(x)) > 0) # NA가 포함된 행의 개수
## [1] 3
sum(is.na(x)) # 데이터셋 전체에서 NA 개수
## [1] 4head(x)
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 NA NA 0.2 setosa
## 2 4.9 3.0 NA 0.2 setosa
## 3 4.7 3.2 1.3 NA setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
x[!complete.cases(x), ] # NA가 포함된 행들 출력
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 NA NA 0.2 setosa
## 2 4.9 3.0 NA 0.2 setosa
## 3 4.7 3.2 1.3 NA setosa
y <- x[complete.cases(x), ] # NA가 포함된 행들 제거
head(y) # 새로운 데이터셋 y의 내용 확인
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
## 7 4.6 3.4 1.4 0.3 setosa
## 8 5.0 3.4 1.5 0.2 setosa
## 9 4.4 2.9 1.4 0.2 setosast <- data.frame(state.x77)
boxplot(st$Income)
boxplot.stats(st$Income)
## $stats
## [1] 3098 3983 4519 4815 5348
##
## $n
## [1] 50
##
## $conf
## [1] 4333.093 4704.907
##
## $out
## [1] 6315
# stats (각 변수의 최소값, 1사분위수, 2사분위수, 3사분위수, 최대값이 저장되어 있는 행렬)
# n (각 그룹마다의 관측값 수를 저장한 벡터)
# conf (중앙값의 95% 신뢰구간, median+-1.58*IQR/(n)^0.5)
# out (이상치)
boxplot.stats(st$Income)$out
## [1] 6315out.val <- boxplot.stats(st$Income)$out # 특이값 추출
st$Income %in% out.val
## [1] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [49] FALSE FALSE
st$Income == out.val
## [1] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [49] FALSE FALSE
st$Income[st$Income %in% out.val] <- NA # 특이값을 NA로 대체
st$Income[st$Income == out.val] <- NA
head(st)
## Population Income Illiteracy Life.Exp Murder HS.Grad Frost Area
## Alabama 3615 3624 2.1 69.05 15.1 41.3 20 50708
## Alaska 365 NA 1.5 69.31 11.3 66.7 152 566432
## Arizona 2212 4530 1.8 70.55 7.8 58.1 15 113417
## Arkansas 2110 3378 1.9 70.66 10.1 39.9 65 51945
## California 21198 5114 1.1 71.71 10.3 62.6 20 156361
## Colorado 2541 4884 0.7 72.06 6.8 63.9 166 103766
newdata <- st[complete.cases(st), ] # NA가 포함된 행 제거 ★★★★★
head(newdata)
## Population Income Illiteracy Life.Exp Murder HS.Grad Frost Area
## Alabama 3615 3624 2.1 69.05 15.1 41.3 20 50708
## Arizona 2212 4530 1.8 70.55 7.8 58.1 15 113417
## Arkansas 2110 3378 1.9 70.66 10.1 39.9 65 51945
## California 21198 5114 1.1 71.71 10.3 62.6 20 156361
## Colorado 2541 4884 0.7 72.06 6.8 63.9 166 103766
## Connecticut 3100 5348 1.1 72.48 3.1 56.0 139 4862v1 <- c(1, 7, 6, 8, 4, 2, 3)
order(v1)
## [1] 1 6 7 5 3 2 4
v1 <- sort(v1) # 오름차순
v1
## [1] 1 2 3 4 6 7 8
v1[order(v1)]
## [1] 1 2 3 4 6 7 8
v2 <- sort(v1, decreasing = T) # 내림차순
v2
## [1] 8 7 6 4 3 2 1head(iris)
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
order(iris$Sepal.Length)
## [1] 14 9 39 43 42 4 7 23 48 3 30 12 13 25 31 46 2 10
## [19] 35 38 58 107 5 8 26 27 36 41 44 50 61 94 1 18 20 22
## [37] 24 40 45 47 99 28 29 33 60 49 6 11 17 21 32 85 34 37
## [55] 54 81 82 90 91 65 67 70 89 95 122 16 19 56 80 96 97 100
## [73] 114 15 68 83 93 102 115 143 62 71 150 63 79 84 86 120 139 64
## [91] 72 74 92 128 135 69 98 127 149 57 73 88 101 104 124 134 137 147
## [109] 52 75 112 116 129 133 138 55 105 111 117 148 59 76 66 78 87 109
## [127] 125 141 145 146 77 113 144 53 121 140 142 51 103 110 126 130 108 131
## [145] 106 118 119 123 136 132
iris[order(iris$Sepal.Length), ] # 오름차순으로 정렬
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 14 4.3 3.0 1.1 0.1 setosa
## 9 4.4 2.9 1.4 0.2 setosa
## 39 4.4 3.0 1.3 0.2 setosa
## 43 4.4 3.2 1.3 0.2 setosa
## 42 4.5 2.3 1.3 0.3 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 7 4.6 3.4 1.4 0.3 setosa
## 23 4.6 3.6 1.0 0.2 setosa
## 48 4.6 3.2 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 30 4.7 3.2 1.6 0.2 setosa
## 12 4.8 3.4 1.6 0.2 setosa
## 13 4.8 3.0 1.4 0.1 setosa
## 25 4.8 3.4 1.9 0.2 setosa
## 31 4.8 3.1 1.6 0.2 setosa
## 46 4.8 3.0 1.4 0.3 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 10 4.9 3.1 1.5 0.1 setosa
## 35 4.9 3.1 1.5 0.2 setosa
## 38 4.9 3.6 1.4 0.1 setosa
## 58 4.9 2.4 3.3 1.0 versicolor
## 107 4.9 2.5 4.5 1.7 virginica
## 5 5.0 3.6 1.4 0.2 setosa
## 8 5.0 3.4 1.5 0.2 setosa
## 26 5.0 3.0 1.6 0.2 setosa
## 27 5.0 3.4 1.6 0.4 setosa
## 36 5.0 3.2 1.2 0.2 setosa
## 41 5.0 3.5 1.3 0.3 setosa
## 44 5.0 3.5 1.6 0.6 setosa
## 50 5.0 3.3 1.4 0.2 setosa
## 61 5.0 2.0 3.5 1.0 versicolor
## 94 5.0 2.3 3.3 1.0 versicolor
## 1 5.1 3.5 1.4 0.2 setosa
## 18 5.1 3.5 1.4 0.3 setosa
## 20 5.1 3.8 1.5 0.3 setosa
## 22 5.1 3.7 1.5 0.4 setosa
## 24 5.1 3.3 1.7 0.5 setosa
## 40 5.1 3.4 1.5 0.2 setosa
## 45 5.1 3.8 1.9 0.4 setosa
## 47 5.1 3.8 1.6 0.2 setosa
## 99 5.1 2.5 3.0 1.1 versicolor
## 28 5.2 3.5 1.5 0.2 setosa
## 29 5.2 3.4 1.4 0.2 setosa
## 33 5.2 4.1 1.5 0.1 setosa
## 60 5.2 2.7 3.9 1.4 versicolor
## 49 5.3 3.7 1.5 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
## 11 5.4 3.7 1.5 0.2 setosa
## 17 5.4 3.9 1.3 0.4 setosa
## 21 5.4 3.4 1.7 0.2 setosa
## 32 5.4 3.4 1.5 0.4 setosa
## 85 5.4 3.0 4.5 1.5 versicolor
## 34 5.5 4.2 1.4 0.2 setosa
## 37 5.5 3.5 1.3 0.2 setosa
## 54 5.5 2.3 4.0 1.3 versicolor
## 81 5.5 2.4 3.8 1.1 versicolor
## 82 5.5 2.4 3.7 1.0 versicolor
## 90 5.5 2.5 4.0 1.3 versicolor
## 91 5.5 2.6 4.4 1.2 versicolor
## 65 5.6 2.9 3.6 1.3 versicolor
## 67 5.6 3.0 4.5 1.5 versicolor
## 70 5.6 2.5 3.9 1.1 versicolor
## 89 5.6 3.0 4.1 1.3 versicolor
## 95 5.6 2.7 4.2 1.3 versicolor
## 122 5.6 2.8 4.9 2.0 virginica
## 16 5.7 4.4 1.5 0.4 setosa
## 19 5.7 3.8 1.7 0.3 setosa
## 56 5.7 2.8 4.5 1.3 versicolor
## 80 5.7 2.6 3.5 1.0 versicolor
## 96 5.7 3.0 4.2 1.2 versicolor
## 97 5.7 2.9 4.2 1.3 versicolor
## 100 5.7 2.8 4.1 1.3 versicolor
## 114 5.7 2.5 5.0 2.0 virginica
## 15 5.8 4.0 1.2 0.2 setosa
## 68 5.8 2.7 4.1 1.0 versicolor
## 83 5.8 2.7 3.9 1.2 versicolor
## 93 5.8 2.6 4.0 1.2 versicolor
## 102 5.8 2.7 5.1 1.9 virginica
## 115 5.8 2.8 5.1 2.4 virginica
## 143 5.8 2.7 5.1 1.9 virginica
## 62 5.9 3.0 4.2 1.5 versicolor
## 71 5.9 3.2 4.8 1.8 versicolor
## 150 5.9 3.0 5.1 1.8 virginica
## 63 6.0 2.2 4.0 1.0 versicolor
## 79 6.0 2.9 4.5 1.5 versicolor
## 84 6.0 2.7 5.1 1.6 versicolor
## 86 6.0 3.4 4.5 1.6 versicolor
## 120 6.0 2.2 5.0 1.5 virginica
## 139 6.0 3.0 4.8 1.8 virginica
## 64 6.1 2.9 4.7 1.4 versicolor
## 72 6.1 2.8 4.0 1.3 versicolor
## 74 6.1 2.8 4.7 1.2 versicolor
## 92 6.1 3.0 4.6 1.4 versicolor
## 128 6.1 3.0 4.9 1.8 virginica
## 135 6.1 2.6 5.6 1.4 virginica
## 69 6.2 2.2 4.5 1.5 versicolor
## 98 6.2 2.9 4.3 1.3 versicolor
## 127 6.2 2.8 4.8 1.8 virginica
## 149 6.2 3.4 5.4 2.3 virginica
## 57 6.3 3.3 4.7 1.6 versicolor
## 73 6.3 2.5 4.9 1.5 versicolor
## 88 6.3 2.3 4.4 1.3 versicolor
## 101 6.3 3.3 6.0 2.5 virginica
## 104 6.3 2.9 5.6 1.8 virginica
## 124 6.3 2.7 4.9 1.8 virginica
## 134 6.3 2.8 5.1 1.5 virginica
## 137 6.3 3.4 5.6 2.4 virginica
## 147 6.3 2.5 5.0 1.9 virginica
## 52 6.4 3.2 4.5 1.5 versicolor
## 75 6.4 2.9 4.3 1.3 versicolor
## 112 6.4 2.7 5.3 1.9 virginica
## 116 6.4 3.2 5.3 2.3 virginica
## 129 6.4 2.8 5.6 2.1 virginica
## 133 6.4 2.8 5.6 2.2 virginica
## 138 6.4 3.1 5.5 1.8 virginica
## 55 6.5 2.8 4.6 1.5 versicolor
## 105 6.5 3.0 5.8 2.2 virginica
## 111 6.5 3.2 5.1 2.0 virginica
## 117 6.5 3.0 5.5 1.8 virginica
## 148 6.5 3.0 5.2 2.0 virginica
## 59 6.6 2.9 4.6 1.3 versicolor
## 76 6.6 3.0 4.4 1.4 versicolor
## 66 6.7 3.1 4.4 1.4 versicolor
## 78 6.7 3.0 5.0 1.7 versicolor
## 87 6.7 3.1 4.7 1.5 versicolor
## 109 6.7 2.5 5.8 1.8 virginica
## 125 6.7 3.3 5.7 2.1 virginica
## 141 6.7 3.1 5.6 2.4 virginica
## 145 6.7 3.3 5.7 2.5 virginica
## 146 6.7 3.0 5.2 2.3 virginica
## 77 6.8 2.8 4.8 1.4 versicolor
## 113 6.8 3.0 5.5 2.1 virginica
## 144 6.8 3.2 5.9 2.3 virginica
## 53 6.9 3.1 4.9 1.5 versicolor
## 121 6.9 3.2 5.7 2.3 virginica
## 140 6.9 3.1 5.4 2.1 virginica
## 142 6.9 3.1 5.1 2.3 virginica
## 51 7.0 3.2 4.7 1.4 versicolor
## 103 7.1 3.0 5.9 2.1 virginica
## 110 7.2 3.6 6.1 2.5 virginica
## 126 7.2 3.2 6.0 1.8 virginica
## 130 7.2 3.0 5.8 1.6 virginica
## 108 7.3 2.9 6.3 1.8 virginica
## 131 7.4 2.8 6.1 1.9 virginica
## 106 7.6 3.0 6.6 2.1 virginica
## 118 7.7 3.8 6.7 2.2 virginica
## 119 7.7 2.6 6.9 2.3 virginica
## 123 7.7 2.8 6.7 2.0 virginica
## 136 7.7 3.0 6.1 2.3 virginica
## 132 7.9 3.8 6.4 2.0 virginica
iris[order(iris$Sepal.Length, decreasing = T), ] # 내림차순으로 정렬
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 132 7.9 3.8 6.4 2.0 virginica
## 118 7.7 3.8 6.7 2.2 virginica
## 119 7.7 2.6 6.9 2.3 virginica
## 123 7.7 2.8 6.7 2.0 virginica
## 136 7.7 3.0 6.1 2.3 virginica
## 106 7.6 3.0 6.6 2.1 virginica
## 131 7.4 2.8 6.1 1.9 virginica
## 108 7.3 2.9 6.3 1.8 virginica
## 110 7.2 3.6 6.1 2.5 virginica
## 126 7.2 3.2 6.0 1.8 virginica
## 130 7.2 3.0 5.8 1.6 virginica
## 103 7.1 3.0 5.9 2.1 virginica
## 51 7.0 3.2 4.7 1.4 versicolor
## 53 6.9 3.1 4.9 1.5 versicolor
## 121 6.9 3.2 5.7 2.3 virginica
## 140 6.9 3.1 5.4 2.1 virginica
## 142 6.9 3.1 5.1 2.3 virginica
## 77 6.8 2.8 4.8 1.4 versicolor
## 113 6.8 3.0 5.5 2.1 virginica
## 144 6.8 3.2 5.9 2.3 virginica
## 66 6.7 3.1 4.4 1.4 versicolor
## 78 6.7 3.0 5.0 1.7 versicolor
## 87 6.7 3.1 4.7 1.5 versicolor
## 109 6.7 2.5 5.8 1.8 virginica
## 125 6.7 3.3 5.7 2.1 virginica
## 141 6.7 3.1 5.6 2.4 virginica
## 145 6.7 3.3 5.7 2.5 virginica
## 146 6.7 3.0 5.2 2.3 virginica
## 59 6.6 2.9 4.6 1.3 versicolor
## 76 6.6 3.0 4.4 1.4 versicolor
## 55 6.5 2.8 4.6 1.5 versicolor
## 105 6.5 3.0 5.8 2.2 virginica
## 111 6.5 3.2 5.1 2.0 virginica
## 117 6.5 3.0 5.5 1.8 virginica
## 148 6.5 3.0 5.2 2.0 virginica
## 52 6.4 3.2 4.5 1.5 versicolor
## 75 6.4 2.9 4.3 1.3 versicolor
## 112 6.4 2.7 5.3 1.9 virginica
## 116 6.4 3.2 5.3 2.3 virginica
## 129 6.4 2.8 5.6 2.1 virginica
## 133 6.4 2.8 5.6 2.2 virginica
## 138 6.4 3.1 5.5 1.8 virginica
## 57 6.3 3.3 4.7 1.6 versicolor
## 73 6.3 2.5 4.9 1.5 versicolor
## 88 6.3 2.3 4.4 1.3 versicolor
## 101 6.3 3.3 6.0 2.5 virginica
## 104 6.3 2.9 5.6 1.8 virginica
## 124 6.3 2.7 4.9 1.8 virginica
## 134 6.3 2.8 5.1 1.5 virginica
## 137 6.3 3.4 5.6 2.4 virginica
## 147 6.3 2.5 5.0 1.9 virginica
## 69 6.2 2.2 4.5 1.5 versicolor
## 98 6.2 2.9 4.3 1.3 versicolor
## 127 6.2 2.8 4.8 1.8 virginica
## 149 6.2 3.4 5.4 2.3 virginica
## 64 6.1 2.9 4.7 1.4 versicolor
## 72 6.1 2.8 4.0 1.3 versicolor
## 74 6.1 2.8 4.7 1.2 versicolor
## 92 6.1 3.0 4.6 1.4 versicolor
## 128 6.1 3.0 4.9 1.8 virginica
## 135 6.1 2.6 5.6 1.4 virginica
## 63 6.0 2.2 4.0 1.0 versicolor
## 79 6.0 2.9 4.5 1.5 versicolor
## 84 6.0 2.7 5.1 1.6 versicolor
## 86 6.0 3.4 4.5 1.6 versicolor
## 120 6.0 2.2 5.0 1.5 virginica
## 139 6.0 3.0 4.8 1.8 virginica
## 62 5.9 3.0 4.2 1.5 versicolor
## 71 5.9 3.2 4.8 1.8 versicolor
## 150 5.9 3.0 5.1 1.8 virginica
## 15 5.8 4.0 1.2 0.2 setosa
## 68 5.8 2.7 4.1 1.0 versicolor
## 83 5.8 2.7 3.9 1.2 versicolor
## 93 5.8 2.6 4.0 1.2 versicolor
## 102 5.8 2.7 5.1 1.9 virginica
## 115 5.8 2.8 5.1 2.4 virginica
## 143 5.8 2.7 5.1 1.9 virginica
## 16 5.7 4.4 1.5 0.4 setosa
## 19 5.7 3.8 1.7 0.3 setosa
## 56 5.7 2.8 4.5 1.3 versicolor
## 80 5.7 2.6 3.5 1.0 versicolor
## 96 5.7 3.0 4.2 1.2 versicolor
## 97 5.7 2.9 4.2 1.3 versicolor
## 100 5.7 2.8 4.1 1.3 versicolor
## 114 5.7 2.5 5.0 2.0 virginica
## 65 5.6 2.9 3.6 1.3 versicolor
## 67 5.6 3.0 4.5 1.5 versicolor
## 70 5.6 2.5 3.9 1.1 versicolor
## 89 5.6 3.0 4.1 1.3 versicolor
## 95 5.6 2.7 4.2 1.3 versicolor
## 122 5.6 2.8 4.9 2.0 virginica
## 34 5.5 4.2 1.4 0.2 setosa
## 37 5.5 3.5 1.3 0.2 setosa
## 54 5.5 2.3 4.0 1.3 versicolor
## 81 5.5 2.4 3.8 1.1 versicolor
## 82 5.5 2.4 3.7 1.0 versicolor
## 90 5.5 2.5 4.0 1.3 versicolor
## 91 5.5 2.6 4.4 1.2 versicolor
## 6 5.4 3.9 1.7 0.4 setosa
## 11 5.4 3.7 1.5 0.2 setosa
## 17 5.4 3.9 1.3 0.4 setosa
## 21 5.4 3.4 1.7 0.2 setosa
## 32 5.4 3.4 1.5 0.4 setosa
## 85 5.4 3.0 4.5 1.5 versicolor
## 49 5.3 3.7 1.5 0.2 setosa
## 28 5.2 3.5 1.5 0.2 setosa
## 29 5.2 3.4 1.4 0.2 setosa
## 33 5.2 4.1 1.5 0.1 setosa
## 60 5.2 2.7 3.9 1.4 versicolor
## 1 5.1 3.5 1.4 0.2 setosa
## 18 5.1 3.5 1.4 0.3 setosa
## 20 5.1 3.8 1.5 0.3 setosa
## 22 5.1 3.7 1.5 0.4 setosa
## 24 5.1 3.3 1.7 0.5 setosa
## 40 5.1 3.4 1.5 0.2 setosa
## 45 5.1 3.8 1.9 0.4 setosa
## 47 5.1 3.8 1.6 0.2 setosa
## 99 5.1 2.5 3.0 1.1 versicolor
## 5 5.0 3.6 1.4 0.2 setosa
## 8 5.0 3.4 1.5 0.2 setosa
## 26 5.0 3.0 1.6 0.2 setosa
## 27 5.0 3.4 1.6 0.4 setosa
## 36 5.0 3.2 1.2 0.2 setosa
## 41 5.0 3.5 1.3 0.3 setosa
## 44 5.0 3.5 1.6 0.6 setosa
## 50 5.0 3.3 1.4 0.2 setosa
## 61 5.0 2.0 3.5 1.0 versicolor
## 94 5.0 2.3 3.3 1.0 versicolor
## 2 4.9 3.0 1.4 0.2 setosa
## 10 4.9 3.1 1.5 0.1 setosa
## 35 4.9 3.1 1.5 0.2 setosa
## 38 4.9 3.6 1.4 0.1 setosa
## 58 4.9 2.4 3.3 1.0 versicolor
## 107 4.9 2.5 4.5 1.7 virginica
## 12 4.8 3.4 1.6 0.2 setosa
## 13 4.8 3.0 1.4 0.1 setosa
## 25 4.8 3.4 1.9 0.2 setosa
## 31 4.8 3.1 1.6 0.2 setosa
## 46 4.8 3.0 1.4 0.3 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 30 4.7 3.2 1.6 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 7 4.6 3.4 1.4 0.3 setosa
## 23 4.6 3.6 1.0 0.2 setosa
## 48 4.6 3.2 1.4 0.2 setosa
## 42 4.5 2.3 1.3 0.3 setosa
## 9 4.4 2.9 1.4 0.2 setosa
## 39 4.4 3.0 1.3 0.2 setosa
## 43 4.4 3.2 1.3 0.2 setosa
## 14 4.3 3.0 1.1 0.1 setosa
iris.new <- iris[order(iris$Sepal.Length), ] # 정렬된 데이터를 저장
head(iris.new)
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 14 4.3 3.0 1.1 0.1 setosa
## 9 4.4 2.9 1.4 0.2 setosa
## 39 4.4 3.0 1.3 0.2 setosa
## 43 4.4 3.2 1.3 0.2 setosa
## 42 4.5 2.3 1.3 0.3 setosa
## 4 4.6 3.1 1.5 0.2 setosa
iris[order(iris$Species,-iris$Petal.Length, decreasing = T), ] # 정렬 기준이 2개
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 107 4.9 2.5 4.5 1.7 virginica
## 127 6.2 2.8 4.8 1.8 virginica
## 139 6.0 3.0 4.8 1.8 virginica
## 122 5.6 2.8 4.9 2.0 virginica
## 124 6.3 2.7 4.9 1.8 virginica
## 128 6.1 3.0 4.9 1.8 virginica
## 114 5.7 2.5 5.0 2.0 virginica
## 120 6.0 2.2 5.0 1.5 virginica
## 147 6.3 2.5 5.0 1.9 virginica
## 102 5.8 2.7 5.1 1.9 virginica
## 111 6.5 3.2 5.1 2.0 virginica
## 115 5.8 2.8 5.1 2.4 virginica
## 134 6.3 2.8 5.1 1.5 virginica
## 142 6.9 3.1 5.1 2.3 virginica
## 143 5.8 2.7 5.1 1.9 virginica
## 150 5.9 3.0 5.1 1.8 virginica
## 146 6.7 3.0 5.2 2.3 virginica
## 148 6.5 3.0 5.2 2.0 virginica
## 112 6.4 2.7 5.3 1.9 virginica
## 116 6.4 3.2 5.3 2.3 virginica
## 140 6.9 3.1 5.4 2.1 virginica
## 149 6.2 3.4 5.4 2.3 virginica
## 113 6.8 3.0 5.5 2.1 virginica
## 117 6.5 3.0 5.5 1.8 virginica
## 138 6.4 3.1 5.5 1.8 virginica
## 104 6.3 2.9 5.6 1.8 virginica
## 129 6.4 2.8 5.6 2.1 virginica
## 133 6.4 2.8 5.6 2.2 virginica
## 135 6.1 2.6 5.6 1.4 virginica
## 137 6.3 3.4 5.6 2.4 virginica
## 141 6.7 3.1 5.6 2.4 virginica
## 121 6.9 3.2 5.7 2.3 virginica
## 125 6.7 3.3 5.7 2.1 virginica
## 145 6.7 3.3 5.7 2.5 virginica
## 105 6.5 3.0 5.8 2.2 virginica
## 109 6.7 2.5 5.8 1.8 virginica
## 130 7.2 3.0 5.8 1.6 virginica
## 103 7.1 3.0 5.9 2.1 virginica
## 144 6.8 3.2 5.9 2.3 virginica
## 101 6.3 3.3 6.0 2.5 virginica
## 126 7.2 3.2 6.0 1.8 virginica
## 110 7.2 3.6 6.1 2.5 virginica
## 131 7.4 2.8 6.1 1.9 virginica
## 136 7.7 3.0 6.1 2.3 virginica
## 108 7.3 2.9 6.3 1.8 virginica
## 132 7.9 3.8 6.4 2.0 virginica
## 106 7.6 3.0 6.6 2.1 virginica
## 118 7.7 3.8 6.7 2.2 virginica
## 123 7.7 2.8 6.7 2.0 virginica
## 119 7.7 2.6 6.9 2.3 virginica
## 99 5.1 2.5 3.0 1.1 versicolor
## 58 4.9 2.4 3.3 1.0 versicolor
## 94 5.0 2.3 3.3 1.0 versicolor
## 61 5.0 2.0 3.5 1.0 versicolor
## 80 5.7 2.6 3.5 1.0 versicolor
## 65 5.6 2.9 3.6 1.3 versicolor
## 82 5.5 2.4 3.7 1.0 versicolor
## 81 5.5 2.4 3.8 1.1 versicolor
## 60 5.2 2.7 3.9 1.4 versicolor
## 70 5.6 2.5 3.9 1.1 versicolor
## 83 5.8 2.7 3.9 1.2 versicolor
## 54 5.5 2.3 4.0 1.3 versicolor
## 63 6.0 2.2 4.0 1.0 versicolor
## 72 6.1 2.8 4.0 1.3 versicolor
## 90 5.5 2.5 4.0 1.3 versicolor
## 93 5.8 2.6 4.0 1.2 versicolor
## 68 5.8 2.7 4.1 1.0 versicolor
## 89 5.6 3.0 4.1 1.3 versicolor
## 100 5.7 2.8 4.1 1.3 versicolor
## 62 5.9 3.0 4.2 1.5 versicolor
## 95 5.6 2.7 4.2 1.3 versicolor
## 96 5.7 3.0 4.2 1.2 versicolor
## 97 5.7 2.9 4.2 1.3 versicolor
## 75 6.4 2.9 4.3 1.3 versicolor
## 98 6.2 2.9 4.3 1.3 versicolor
## 66 6.7 3.1 4.4 1.4 versicolor
## 76 6.6 3.0 4.4 1.4 versicolor
## 88 6.3 2.3 4.4 1.3 versicolor
## 91 5.5 2.6 4.4 1.2 versicolor
## 52 6.4 3.2 4.5 1.5 versicolor
## 56 5.7 2.8 4.5 1.3 versicolor
## 67 5.6 3.0 4.5 1.5 versicolor
## 69 6.2 2.2 4.5 1.5 versicolor
## 79 6.0 2.9 4.5 1.5 versicolor
## 85 5.4 3.0 4.5 1.5 versicolor
## 86 6.0 3.4 4.5 1.6 versicolor
## 55 6.5 2.8 4.6 1.5 versicolor
## 59 6.6 2.9 4.6 1.3 versicolor
## 92 6.1 3.0 4.6 1.4 versicolor
## 51 7.0 3.2 4.7 1.4 versicolor
## 57 6.3 3.3 4.7 1.6 versicolor
## 64 6.1 2.9 4.7 1.4 versicolor
## 74 6.1 2.8 4.7 1.2 versicolor
## 87 6.7 3.1 4.7 1.5 versicolor
## 71 5.9 3.2 4.8 1.8 versicolor
## 77 6.8 2.8 4.8 1.4 versicolor
## 53 6.9 3.1 4.9 1.5 versicolor
## 73 6.3 2.5 4.9 1.5 versicolor
## 78 6.7 3.0 5.0 1.7 versicolor
## 84 6.0 2.7 5.1 1.6 versicolor
## 23 4.6 3.6 1.0 0.2 setosa
## 14 4.3 3.0 1.1 0.1 setosa
## 15 5.8 4.0 1.2 0.2 setosa
## 36 5.0 3.2 1.2 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 17 5.4 3.9 1.3 0.4 setosa
## 37 5.5 3.5 1.3 0.2 setosa
## 39 4.4 3.0 1.3 0.2 setosa
## 41 5.0 3.5 1.3 0.3 setosa
## 42 4.5 2.3 1.3 0.3 setosa
## 43 4.4 3.2 1.3 0.2 setosa
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 7 4.6 3.4 1.4 0.3 setosa
## 9 4.4 2.9 1.4 0.2 setosa
## 13 4.8 3.0 1.4 0.1 setosa
## 18 5.1 3.5 1.4 0.3 setosa
## 29 5.2 3.4 1.4 0.2 setosa
## 34 5.5 4.2 1.4 0.2 setosa
## 38 4.9 3.6 1.4 0.1 setosa
## 46 4.8 3.0 1.4 0.3 setosa
## 48 4.6 3.2 1.4 0.2 setosa
## 50 5.0 3.3 1.4 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 8 5.0 3.4 1.5 0.2 setosa
## 10 4.9 3.1 1.5 0.1 setosa
## 11 5.4 3.7 1.5 0.2 setosa
## 16 5.7 4.4 1.5 0.4 setosa
## 20 5.1 3.8 1.5 0.3 setosa
## 22 5.1 3.7 1.5 0.4 setosa
## 28 5.2 3.5 1.5 0.2 setosa
## 32 5.4 3.4 1.5 0.4 setosa
## 33 5.2 4.1 1.5 0.1 setosa
## 35 4.9 3.1 1.5 0.2 setosa
## 40 5.1 3.4 1.5 0.2 setosa
## 49 5.3 3.7 1.5 0.2 setosa
## 12 4.8 3.4 1.6 0.2 setosa
## 26 5.0 3.0 1.6 0.2 setosa
## 27 5.0 3.4 1.6 0.4 setosa
## 30 4.7 3.2 1.6 0.2 setosa
## 31 4.8 3.1 1.6 0.2 setosa
## 44 5.0 3.5 1.6 0.6 setosa
## 47 5.1 3.8 1.6 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
## 19 5.7 3.8 1.7 0.3 setosa
## 21 5.4 3.4 1.7 0.2 setosa
## 24 5.1 3.3 1.7 0.5 setosa
## 25 4.8 3.4 1.9 0.2 setosa
## 45 5.1 3.8 1.9 0.4 setosa
iris[order(iris$Species, decreasing = T, iris$Petal.Length), ]
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 119 7.7 2.6 6.9 2.3 virginica
## 118 7.7 3.8 6.7 2.2 virginica
## 123 7.7 2.8 6.7 2.0 virginica
## 106 7.6 3.0 6.6 2.1 virginica
## 132 7.9 3.8 6.4 2.0 virginica
## 108 7.3 2.9 6.3 1.8 virginica
## 110 7.2 3.6 6.1 2.5 virginica
## 131 7.4 2.8 6.1 1.9 virginica
## 136 7.7 3.0 6.1 2.3 virginica
## 101 6.3 3.3 6.0 2.5 virginica
## 126 7.2 3.2 6.0 1.8 virginica
## 103 7.1 3.0 5.9 2.1 virginica
## 144 6.8 3.2 5.9 2.3 virginica
## 105 6.5 3.0 5.8 2.2 virginica
## 109 6.7 2.5 5.8 1.8 virginica
## 130 7.2 3.0 5.8 1.6 virginica
## 121 6.9 3.2 5.7 2.3 virginica
## 125 6.7 3.3 5.7 2.1 virginica
## 145 6.7 3.3 5.7 2.5 virginica
## 104 6.3 2.9 5.6 1.8 virginica
## 129 6.4 2.8 5.6 2.1 virginica
## 133 6.4 2.8 5.6 2.2 virginica
## 135 6.1 2.6 5.6 1.4 virginica
## 137 6.3 3.4 5.6 2.4 virginica
## 141 6.7 3.1 5.6 2.4 virginica
## 113 6.8 3.0 5.5 2.1 virginica
## 117 6.5 3.0 5.5 1.8 virginica
## 138 6.4 3.1 5.5 1.8 virginica
## 140 6.9 3.1 5.4 2.1 virginica
## 149 6.2 3.4 5.4 2.3 virginica
## 112 6.4 2.7 5.3 1.9 virginica
## 116 6.4 3.2 5.3 2.3 virginica
## 146 6.7 3.0 5.2 2.3 virginica
## 148 6.5 3.0 5.2 2.0 virginica
## 102 5.8 2.7 5.1 1.9 virginica
## 111 6.5 3.2 5.1 2.0 virginica
## 115 5.8 2.8 5.1 2.4 virginica
## 134 6.3 2.8 5.1 1.5 virginica
## 142 6.9 3.1 5.1 2.3 virginica
## 143 5.8 2.7 5.1 1.9 virginica
## 150 5.9 3.0 5.1 1.8 virginica
## 114 5.7 2.5 5.0 2.0 virginica
## 120 6.0 2.2 5.0 1.5 virginica
## 147 6.3 2.5 5.0 1.9 virginica
## 122 5.6 2.8 4.9 2.0 virginica
## 124 6.3 2.7 4.9 1.8 virginica
## 128 6.1 3.0 4.9 1.8 virginica
## 127 6.2 2.8 4.8 1.8 virginica
## 139 6.0 3.0 4.8 1.8 virginica
## 107 4.9 2.5 4.5 1.7 virginica
## 84 6.0 2.7 5.1 1.6 versicolor
## 78 6.7 3.0 5.0 1.7 versicolor
## 53 6.9 3.1 4.9 1.5 versicolor
## 73 6.3 2.5 4.9 1.5 versicolor
## 71 5.9 3.2 4.8 1.8 versicolor
## 77 6.8 2.8 4.8 1.4 versicolor
## 51 7.0 3.2 4.7 1.4 versicolor
## 57 6.3 3.3 4.7 1.6 versicolor
## 64 6.1 2.9 4.7 1.4 versicolor
## 74 6.1 2.8 4.7 1.2 versicolor
## 87 6.7 3.1 4.7 1.5 versicolor
## 55 6.5 2.8 4.6 1.5 versicolor
## 59 6.6 2.9 4.6 1.3 versicolor
## 92 6.1 3.0 4.6 1.4 versicolor
## 52 6.4 3.2 4.5 1.5 versicolor
## 56 5.7 2.8 4.5 1.3 versicolor
## 67 5.6 3.0 4.5 1.5 versicolor
## 69 6.2 2.2 4.5 1.5 versicolor
## 79 6.0 2.9 4.5 1.5 versicolor
## 85 5.4 3.0 4.5 1.5 versicolor
## 86 6.0 3.4 4.5 1.6 versicolor
## 66 6.7 3.1 4.4 1.4 versicolor
## 76 6.6 3.0 4.4 1.4 versicolor
## 88 6.3 2.3 4.4 1.3 versicolor
## 91 5.5 2.6 4.4 1.2 versicolor
## 75 6.4 2.9 4.3 1.3 versicolor
## 98 6.2 2.9 4.3 1.3 versicolor
## 62 5.9 3.0 4.2 1.5 versicolor
## 95 5.6 2.7 4.2 1.3 versicolor
## 96 5.7 3.0 4.2 1.2 versicolor
## 97 5.7 2.9 4.2 1.3 versicolor
## 68 5.8 2.7 4.1 1.0 versicolor
## 89 5.6 3.0 4.1 1.3 versicolor
## 100 5.7 2.8 4.1 1.3 versicolor
## 54 5.5 2.3 4.0 1.3 versicolor
## 63 6.0 2.2 4.0 1.0 versicolor
## 72 6.1 2.8 4.0 1.3 versicolor
## 90 5.5 2.5 4.0 1.3 versicolor
## 93 5.8 2.6 4.0 1.2 versicolor
## 60 5.2 2.7 3.9 1.4 versicolor
## 70 5.6 2.5 3.9 1.1 versicolor
## 83 5.8 2.7 3.9 1.2 versicolor
## 81 5.5 2.4 3.8 1.1 versicolor
## 82 5.5 2.4 3.7 1.0 versicolor
## 65 5.6 2.9 3.6 1.3 versicolor
## 61 5.0 2.0 3.5 1.0 versicolor
## 80 5.7 2.6 3.5 1.0 versicolor
## 58 4.9 2.4 3.3 1.0 versicolor
## 94 5.0 2.3 3.3 1.0 versicolor
## 99 5.1 2.5 3.0 1.1 versicolor
## 25 4.8 3.4 1.9 0.2 setosa
## 45 5.1 3.8 1.9 0.4 setosa
## 6 5.4 3.9 1.7 0.4 setosa
## 19 5.7 3.8 1.7 0.3 setosa
## 21 5.4 3.4 1.7 0.2 setosa
## 24 5.1 3.3 1.7 0.5 setosa
## 12 4.8 3.4 1.6 0.2 setosa
## 26 5.0 3.0 1.6 0.2 setosa
## 27 5.0 3.4 1.6 0.4 setosa
## 30 4.7 3.2 1.6 0.2 setosa
## 31 4.8 3.1 1.6 0.2 setosa
## 44 5.0 3.5 1.6 0.6 setosa
## 47 5.1 3.8 1.6 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 8 5.0 3.4 1.5 0.2 setosa
## 10 4.9 3.1 1.5 0.1 setosa
## 11 5.4 3.7 1.5 0.2 setosa
## 16 5.7 4.4 1.5 0.4 setosa
## 20 5.1 3.8 1.5 0.3 setosa
## 22 5.1 3.7 1.5 0.4 setosa
## 28 5.2 3.5 1.5 0.2 setosa
## 32 5.4 3.4 1.5 0.4 setosa
## 33 5.2 4.1 1.5 0.1 setosa
## 35 4.9 3.1 1.5 0.2 setosa
## 40 5.1 3.4 1.5 0.2 setosa
## 49 5.3 3.7 1.5 0.2 setosa
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 7 4.6 3.4 1.4 0.3 setosa
## 9 4.4 2.9 1.4 0.2 setosa
## 13 4.8 3.0 1.4 0.1 setosa
## 18 5.1 3.5 1.4 0.3 setosa
## 29 5.2 3.4 1.4 0.2 setosa
## 34 5.5 4.2 1.4 0.2 setosa
## 38 4.9 3.6 1.4 0.1 setosa
## 46 4.8 3.0 1.4 0.3 setosa
## 48 4.6 3.2 1.4 0.2 setosa
## 50 5.0 3.3 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 17 5.4 3.9 1.3 0.4 setosa
## 37 5.5 3.5 1.3 0.2 setosa
## 39 4.4 3.0 1.3 0.2 setosa
## 41 5.0 3.5 1.3 0.3 setosa
## 42 4.5 2.3 1.3 0.3 setosa
## 43 4.4 3.2 1.3 0.2 setosa
## 15 5.8 4.0 1.2 0.2 setosa
## 36 5.0 3.2 1.2 0.2 setosa
## 14 4.3 3.0 1.1 0.1 setosa
## 23 4.6 3.6 1.0 0.2 setosasp <- split(iris, iris$Species) # 품종별로 데이터 분리
sp # 분리 결과 확인
## $setosa
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
## 7 4.6 3.4 1.4 0.3 setosa
## 8 5.0 3.4 1.5 0.2 setosa
## 9 4.4 2.9 1.4 0.2 setosa
## 10 4.9 3.1 1.5 0.1 setosa
## 11 5.4 3.7 1.5 0.2 setosa
## 12 4.8 3.4 1.6 0.2 setosa
## 13 4.8 3.0 1.4 0.1 setosa
## 14 4.3 3.0 1.1 0.1 setosa
## 15 5.8 4.0 1.2 0.2 setosa
## 16 5.7 4.4 1.5 0.4 setosa
## 17 5.4 3.9 1.3 0.4 setosa
## 18 5.1 3.5 1.4 0.3 setosa
## 19 5.7 3.8 1.7 0.3 setosa
## 20 5.1 3.8 1.5 0.3 setosa
## 21 5.4 3.4 1.7 0.2 setosa
## 22 5.1 3.7 1.5 0.4 setosa
## 23 4.6 3.6 1.0 0.2 setosa
## 24 5.1 3.3 1.7 0.5 setosa
## 25 4.8 3.4 1.9 0.2 setosa
## 26 5.0 3.0 1.6 0.2 setosa
## 27 5.0 3.4 1.6 0.4 setosa
## 28 5.2 3.5 1.5 0.2 setosa
## 29 5.2 3.4 1.4 0.2 setosa
## 30 4.7 3.2 1.6 0.2 setosa
## 31 4.8 3.1 1.6 0.2 setosa
## 32 5.4 3.4 1.5 0.4 setosa
## 33 5.2 4.1 1.5 0.1 setosa
## 34 5.5 4.2 1.4 0.2 setosa
## 35 4.9 3.1 1.5 0.2 setosa
## 36 5.0 3.2 1.2 0.2 setosa
## 37 5.5 3.5 1.3 0.2 setosa
## 38 4.9 3.6 1.4 0.1 setosa
## 39 4.4 3.0 1.3 0.2 setosa
## 40 5.1 3.4 1.5 0.2 setosa
## 41 5.0 3.5 1.3 0.3 setosa
## 42 4.5 2.3 1.3 0.3 setosa
## 43 4.4 3.2 1.3 0.2 setosa
## 44 5.0 3.5 1.6 0.6 setosa
## 45 5.1 3.8 1.9 0.4 setosa
## 46 4.8 3.0 1.4 0.3 setosa
## 47 5.1 3.8 1.6 0.2 setosa
## 48 4.6 3.2 1.4 0.2 setosa
## 49 5.3 3.7 1.5 0.2 setosa
## 50 5.0 3.3 1.4 0.2 setosa
##
## $versicolor
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 51 7.0 3.2 4.7 1.4 versicolor
## 52 6.4 3.2 4.5 1.5 versicolor
## 53 6.9 3.1 4.9 1.5 versicolor
## 54 5.5 2.3 4.0 1.3 versicolor
## 55 6.5 2.8 4.6 1.5 versicolor
## 56 5.7 2.8 4.5 1.3 versicolor
## 57 6.3 3.3 4.7 1.6 versicolor
## 58 4.9 2.4 3.3 1.0 versicolor
## 59 6.6 2.9 4.6 1.3 versicolor
## 60 5.2 2.7 3.9 1.4 versicolor
## 61 5.0 2.0 3.5 1.0 versicolor
## 62 5.9 3.0 4.2 1.5 versicolor
## 63 6.0 2.2 4.0 1.0 versicolor
## 64 6.1 2.9 4.7 1.4 versicolor
## 65 5.6 2.9 3.6 1.3 versicolor
## 66 6.7 3.1 4.4 1.4 versicolor
## 67 5.6 3.0 4.5 1.5 versicolor
## 68 5.8 2.7 4.1 1.0 versicolor
## 69 6.2 2.2 4.5 1.5 versicolor
## 70 5.6 2.5 3.9 1.1 versicolor
## 71 5.9 3.2 4.8 1.8 versicolor
## 72 6.1 2.8 4.0 1.3 versicolor
## 73 6.3 2.5 4.9 1.5 versicolor
## 74 6.1 2.8 4.7 1.2 versicolor
## 75 6.4 2.9 4.3 1.3 versicolor
## 76 6.6 3.0 4.4 1.4 versicolor
## 77 6.8 2.8 4.8 1.4 versicolor
## 78 6.7 3.0 5.0 1.7 versicolor
## 79 6.0 2.9 4.5 1.5 versicolor
## 80 5.7 2.6 3.5 1.0 versicolor
## 81 5.5 2.4 3.8 1.1 versicolor
## 82 5.5 2.4 3.7 1.0 versicolor
## 83 5.8 2.7 3.9 1.2 versicolor
## 84 6.0 2.7 5.1 1.6 versicolor
## 85 5.4 3.0 4.5 1.5 versicolor
## 86 6.0 3.4 4.5 1.6 versicolor
## 87 6.7 3.1 4.7 1.5 versicolor
## 88 6.3 2.3 4.4 1.3 versicolor
## 89 5.6 3.0 4.1 1.3 versicolor
## 90 5.5 2.5 4.0 1.3 versicolor
## 91 5.5 2.6 4.4 1.2 versicolor
## 92 6.1 3.0 4.6 1.4 versicolor
## 93 5.8 2.6 4.0 1.2 versicolor
## 94 5.0 2.3 3.3 1.0 versicolor
## 95 5.6 2.7 4.2 1.3 versicolor
## 96 5.7 3.0 4.2 1.2 versicolor
## 97 5.7 2.9 4.2 1.3 versicolor
## 98 6.2 2.9 4.3 1.3 versicolor
## 99 5.1 2.5 3.0 1.1 versicolor
## 100 5.7 2.8 4.1 1.3 versicolor
##
## $virginica
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 101 6.3 3.3 6.0 2.5 virginica
## 102 5.8 2.7 5.1 1.9 virginica
## 103 7.1 3.0 5.9 2.1 virginica
## 104 6.3 2.9 5.6 1.8 virginica
## 105 6.5 3.0 5.8 2.2 virginica
## 106 7.6 3.0 6.6 2.1 virginica
## 107 4.9 2.5 4.5 1.7 virginica
## 108 7.3 2.9 6.3 1.8 virginica
## 109 6.7 2.5 5.8 1.8 virginica
## 110 7.2 3.6 6.1 2.5 virginica
## 111 6.5 3.2 5.1 2.0 virginica
## 112 6.4 2.7 5.3 1.9 virginica
## 113 6.8 3.0 5.5 2.1 virginica
## 114 5.7 2.5 5.0 2.0 virginica
## 115 5.8 2.8 5.1 2.4 virginica
## 116 6.4 3.2 5.3 2.3 virginica
## 117 6.5 3.0 5.5 1.8 virginica
## 118 7.7 3.8 6.7 2.2 virginica
## 119 7.7 2.6 6.9 2.3 virginica
## 120 6.0 2.2 5.0 1.5 virginica
## 121 6.9 3.2 5.7 2.3 virginica
## 122 5.6 2.8 4.9 2.0 virginica
## 123 7.7 2.8 6.7 2.0 virginica
## 124 6.3 2.7 4.9 1.8 virginica
## 125 6.7 3.3 5.7 2.1 virginica
## 126 7.2 3.2 6.0 1.8 virginica
## 127 6.2 2.8 4.8 1.8 virginica
## 128 6.1 3.0 4.9 1.8 virginica
## 129 6.4 2.8 5.6 2.1 virginica
## 130 7.2 3.0 5.8 1.6 virginica
## 131 7.4 2.8 6.1 1.9 virginica
## 132 7.9 3.8 6.4 2.0 virginica
## 133 6.4 2.8 5.6 2.2 virginica
## 134 6.3 2.8 5.1 1.5 virginica
## 135 6.1 2.6 5.6 1.4 virginica
## 136 7.7 3.0 6.1 2.3 virginica
## 137 6.3 3.4 5.6 2.4 virginica
## 138 6.4 3.1 5.5 1.8 virginica
## 139 6.0 3.0 4.8 1.8 virginica
## 140 6.9 3.1 5.4 2.1 virginica
## 141 6.7 3.1 5.6 2.4 virginica
## 142 6.9 3.1 5.1 2.3 virginica
## 143 5.8 2.7 5.1 1.9 virginica
## 144 6.8 3.2 5.9 2.3 virginica
## 145 6.7 3.3 5.7 2.5 virginica
## 146 6.7 3.0 5.2 2.3 virginica
## 147 6.3 2.5 5.0 1.9 virginica
## 148 6.5 3.0 5.2 2.0 virginica
## 149 6.2 3.4 5.4 2.3 virginica
## 150 5.9 3.0 5.1 1.8 virginica
summary(sp) # 분리 결과 요약
## Length Class Mode
## setosa 5 data.frame list
## versicolor 5 data.frame list
## virginica 5 data.frame list
sp$setosa # setosa 품종의 데이터 확인
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
## 7 4.6 3.4 1.4 0.3 setosa
## 8 5.0 3.4 1.5 0.2 setosa
## 9 4.4 2.9 1.4 0.2 setosa
## 10 4.9 3.1 1.5 0.1 setosa
## 11 5.4 3.7 1.5 0.2 setosa
## 12 4.8 3.4 1.6 0.2 setosa
## 13 4.8 3.0 1.4 0.1 setosa
## 14 4.3 3.0 1.1 0.1 setosa
## 15 5.8 4.0 1.2 0.2 setosa
## 16 5.7 4.4 1.5 0.4 setosa
## 17 5.4 3.9 1.3 0.4 setosa
## 18 5.1 3.5 1.4 0.3 setosa
## 19 5.7 3.8 1.7 0.3 setosa
## 20 5.1 3.8 1.5 0.3 setosa
## 21 5.4 3.4 1.7 0.2 setosa
## 22 5.1 3.7 1.5 0.4 setosa
## 23 4.6 3.6 1.0 0.2 setosa
## 24 5.1 3.3 1.7 0.5 setosa
## 25 4.8 3.4 1.9 0.2 setosa
## 26 5.0 3.0 1.6 0.2 setosa
## 27 5.0 3.4 1.6 0.4 setosa
## 28 5.2 3.5 1.5 0.2 setosa
## 29 5.2 3.4 1.4 0.2 setosa
## 30 4.7 3.2 1.6 0.2 setosa
## 31 4.8 3.1 1.6 0.2 setosa
## 32 5.4 3.4 1.5 0.4 setosa
## 33 5.2 4.1 1.5 0.1 setosa
## 34 5.5 4.2 1.4 0.2 setosa
## 35 4.9 3.1 1.5 0.2 setosa
## 36 5.0 3.2 1.2 0.2 setosa
## 37 5.5 3.5 1.3 0.2 setosa
## 38 4.9 3.6 1.4 0.1 setosa
## 39 4.4 3.0 1.3 0.2 setosa
## 40 5.1 3.4 1.5 0.2 setosa
## 41 5.0 3.5 1.3 0.3 setosa
## 42 4.5 2.3 1.3 0.3 setosa
## 43 4.4 3.2 1.3 0.2 setosa
## 44 5.0 3.5 1.6 0.6 setosa
## 45 5.1 3.8 1.9 0.4 setosa
## 46 4.8 3.0 1.4 0.3 setosa
## 47 5.1 3.8 1.6 0.2 setosa
## 48 4.6 3.2 1.4 0.2 setosa
## 49 5.3 3.7 1.5 0.2 setosa
## 50 5.0 3.3 1.4 0.2 setosa
setosa <- sp$setosasubset(iris, Species == "setosa")
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
## 7 4.6 3.4 1.4 0.3 setosa
## 8 5.0 3.4 1.5 0.2 setosa
## 9 4.4 2.9 1.4 0.2 setosa
## 10 4.9 3.1 1.5 0.1 setosa
## 11 5.4 3.7 1.5 0.2 setosa
## 12 4.8 3.4 1.6 0.2 setosa
## 13 4.8 3.0 1.4 0.1 setosa
## 14 4.3 3.0 1.1 0.1 setosa
## 15 5.8 4.0 1.2 0.2 setosa
## 16 5.7 4.4 1.5 0.4 setosa
## 17 5.4 3.9 1.3 0.4 setosa
## 18 5.1 3.5 1.4 0.3 setosa
## 19 5.7 3.8 1.7 0.3 setosa
## 20 5.1 3.8 1.5 0.3 setosa
## 21 5.4 3.4 1.7 0.2 setosa
## 22 5.1 3.7 1.5 0.4 setosa
## 23 4.6 3.6 1.0 0.2 setosa
## 24 5.1 3.3 1.7 0.5 setosa
## 25 4.8 3.4 1.9 0.2 setosa
## 26 5.0 3.0 1.6 0.2 setosa
## 27 5.0 3.4 1.6 0.4 setosa
## 28 5.2 3.5 1.5 0.2 setosa
## 29 5.2 3.4 1.4 0.2 setosa
## 30 4.7 3.2 1.6 0.2 setosa
## 31 4.8 3.1 1.6 0.2 setosa
## 32 5.4 3.4 1.5 0.4 setosa
## 33 5.2 4.1 1.5 0.1 setosa
## 34 5.5 4.2 1.4 0.2 setosa
## 35 4.9 3.1 1.5 0.2 setosa
## 36 5.0 3.2 1.2 0.2 setosa
## 37 5.5 3.5 1.3 0.2 setosa
## 38 4.9 3.6 1.4 0.1 setosa
## 39 4.4 3.0 1.3 0.2 setosa
## 40 5.1 3.4 1.5 0.2 setosa
## 41 5.0 3.5 1.3 0.3 setosa
## 42 4.5 2.3 1.3 0.3 setosa
## 43 4.4 3.2 1.3 0.2 setosa
## 44 5.0 3.5 1.6 0.6 setosa
## 45 5.1 3.8 1.9 0.4 setosa
## 46 4.8 3.0 1.4 0.3 setosa
## 47 5.1 3.8 1.6 0.2 setosa
## 48 4.6 3.2 1.4 0.2 setosa
## 49 5.3 3.7 1.5 0.2 setosa
## 50 5.0 3.3 1.4 0.2 setosa
subset(iris, Sepal.Length > 7.5)
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 106 7.6 3.0 6.6 2.1 virginica
## 118 7.7 3.8 6.7 2.2 virginica
## 119 7.7 2.6 6.9 2.3 virginica
## 123 7.7 2.8 6.7 2.0 virginica
## 132 7.9 3.8 6.4 2.0 virginica
## 136 7.7 3.0 6.1 2.3 virginica
subset(iris, Sepal.Length > 5.1 &
Sepal.Width > 3.9)
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 15 5.8 4.0 1.2 0.2 setosa
## 16 5.7 4.4 1.5 0.4 setosa
## 33 5.2 4.1 1.5 0.1 setosa
## 34 5.5 4.2 1.4 0.2 setosa
subset(iris, Sepal.Length > 5.1 |
Sepal.Width > 3.9)
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 6 5.4 3.9 1.7 0.4 setosa
## 11 5.4 3.7 1.5 0.2 setosa
## 15 5.8 4.0 1.2 0.2 setosa
## 16 5.7 4.4 1.5 0.4 setosa
## 17 5.4 3.9 1.3 0.4 setosa
## 19 5.7 3.8 1.7 0.3 setosa
## 21 5.4 3.4 1.7 0.2 setosa
## 28 5.2 3.5 1.5 0.2 setosa
## 29 5.2 3.4 1.4 0.2 setosa
## 32 5.4 3.4 1.5 0.4 setosa
## 33 5.2 4.1 1.5 0.1 setosa
## 34 5.5 4.2 1.4 0.2 setosa
## 37 5.5 3.5 1.3 0.2 setosa
## 49 5.3 3.7 1.5 0.2 setosa
## 51 7.0 3.2 4.7 1.4 versicolor
## 52 6.4 3.2 4.5 1.5 versicolor
## 53 6.9 3.1 4.9 1.5 versicolor
## 54 5.5 2.3 4.0 1.3 versicolor
## 55 6.5 2.8 4.6 1.5 versicolor
## 56 5.7 2.8 4.5 1.3 versicolor
## 57 6.3 3.3 4.7 1.6 versicolor
## 59 6.6 2.9 4.6 1.3 versicolor
## 60 5.2 2.7 3.9 1.4 versicolor
## 62 5.9 3.0 4.2 1.5 versicolor
## 63 6.0 2.2 4.0 1.0 versicolor
## 64 6.1 2.9 4.7 1.4 versicolor
## 65 5.6 2.9 3.6 1.3 versicolor
## 66 6.7 3.1 4.4 1.4 versicolor
## 67 5.6 3.0 4.5 1.5 versicolor
## 68 5.8 2.7 4.1 1.0 versicolor
## 69 6.2 2.2 4.5 1.5 versicolor
## 70 5.6 2.5 3.9 1.1 versicolor
## 71 5.9 3.2 4.8 1.8 versicolor
## 72 6.1 2.8 4.0 1.3 versicolor
## 73 6.3 2.5 4.9 1.5 versicolor
## 74 6.1 2.8 4.7 1.2 versicolor
## 75 6.4 2.9 4.3 1.3 versicolor
## 76 6.6 3.0 4.4 1.4 versicolor
## 77 6.8 2.8 4.8 1.4 versicolor
## 78 6.7 3.0 5.0 1.7 versicolor
## 79 6.0 2.9 4.5 1.5 versicolor
## 80 5.7 2.6 3.5 1.0 versicolor
## 81 5.5 2.4 3.8 1.1 versicolor
## 82 5.5 2.4 3.7 1.0 versicolor
## 83 5.8 2.7 3.9 1.2 versicolor
## 84 6.0 2.7 5.1 1.6 versicolor
## 85 5.4 3.0 4.5 1.5 versicolor
## 86 6.0 3.4 4.5 1.6 versicolor
## 87 6.7 3.1 4.7 1.5 versicolor
## 88 6.3 2.3 4.4 1.3 versicolor
## 89 5.6 3.0 4.1 1.3 versicolor
## 90 5.5 2.5 4.0 1.3 versicolor
## 91 5.5 2.6 4.4 1.2 versicolor
## 92 6.1 3.0 4.6 1.4 versicolor
## 93 5.8 2.6 4.0 1.2 versicolor
## 95 5.6 2.7 4.2 1.3 versicolor
## 96 5.7 3.0 4.2 1.2 versicolor
## 97 5.7 2.9 4.2 1.3 versicolor
## 98 6.2 2.9 4.3 1.3 versicolor
## 100 5.7 2.8 4.1 1.3 versicolor
## 101 6.3 3.3 6.0 2.5 virginica
## 102 5.8 2.7 5.1 1.9 virginica
## 103 7.1 3.0 5.9 2.1 virginica
## 104 6.3 2.9 5.6 1.8 virginica
## 105 6.5 3.0 5.8 2.2 virginica
## 106 7.6 3.0 6.6 2.1 virginica
## 108 7.3 2.9 6.3 1.8 virginica
## 109 6.7 2.5 5.8 1.8 virginica
## 110 7.2 3.6 6.1 2.5 virginica
## 111 6.5 3.2 5.1 2.0 virginica
## 112 6.4 2.7 5.3 1.9 virginica
## 113 6.8 3.0 5.5 2.1 virginica
## 114 5.7 2.5 5.0 2.0 virginica
## 115 5.8 2.8 5.1 2.4 virginica
## 116 6.4 3.2 5.3 2.3 virginica
## 117 6.5 3.0 5.5 1.8 virginica
## 118 7.7 3.8 6.7 2.2 virginica
## 119 7.7 2.6 6.9 2.3 virginica
## 120 6.0 2.2 5.0 1.5 virginica
## 121 6.9 3.2 5.7 2.3 virginica
## 122 5.6 2.8 4.9 2.0 virginica
## 123 7.7 2.8 6.7 2.0 virginica
## 124 6.3 2.7 4.9 1.8 virginica
## 125 6.7 3.3 5.7 2.1 virginica
## 126 7.2 3.2 6.0 1.8 virginica
## 127 6.2 2.8 4.8 1.8 virginica
## 128 6.1 3.0 4.9 1.8 virginica
## 129 6.4 2.8 5.6 2.1 virginica
## 130 7.2 3.0 5.8 1.6 virginica
## 131 7.4 2.8 6.1 1.9 virginica
## 132 7.9 3.8 6.4 2.0 virginica
## 133 6.4 2.8 5.6 2.2 virginica
## 134 6.3 2.8 5.1 1.5 virginica
## 135 6.1 2.6 5.6 1.4 virginica
## 136 7.7 3.0 6.1 2.3 virginica
## 137 6.3 3.4 5.6 2.4 virginica
## 138 6.4 3.1 5.5 1.8 virginica
## 139 6.0 3.0 4.8 1.8 virginica
## 140 6.9 3.1 5.4 2.1 virginica
## 141 6.7 3.1 5.6 2.4 virginica
## 142 6.9 3.1 5.1 2.3 virginica
## 143 5.8 2.7 5.1 1.9 virginica
## 144 6.8 3.2 5.9 2.3 virginica
## 145 6.7 3.3 5.7 2.5 virginica
## 146 6.7 3.0 5.2 2.3 virginica
## 147 6.3 2.5 5.0 1.9 virginica
## 148 6.5 3.0 5.2 2.0 virginica
## 149 6.2 3.4 5.4 2.3 virginica
## 150 5.9 3.0 5.1 1.8 virginica
subset(iris, Sepal.Length > 7.6,
select = c(Petal.Length, Petal.Width))
## Petal.Length Petal.Width
## 118 6.7 2.2
## 119 6.9 2.3
## 123 6.7 2.0
## 132 6.4 2.0
## 136 6.1 2.3x <- 1:10
sample(x, size = 5, replace = FALSE) # 비복원추출
## [1] 1 9 4 10 7
sample(x, size = 5, replace = TRUE)
## [1] 5 1 2 3 5
x <- 1:45
sample(x, size = 6, replace = FALSE)
## [1] 20 11 23 22 7 30idx <- sample(1:nrow(iris), size = 50,
replace = FALSE)
iris.50 <- iris[idx, ] # 50개의 행 추출
dim(iris.50) # 행과 열의 개수 확인
## [1] 50 5
head(iris.50)
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 98 6.2 2.9 4.3 1.3 versicolor
## 9 4.4 2.9 1.4 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 46 4.8 3.0 1.4 0.3 setosa
## 37 5.5 3.5 1.3 0.2 setosa
## 90 5.5 2.5 4.0 1.3 versicolorsample(1:20, size = 5)
## [1] 16 5 15 8 1
sample(1:20, size = 5)
## [1] 13 20 18 14 6
sample(1:20, size = 5)
## [1] 8 11 7 19 15
# 같은 값이 추출되도록 고정시키고 싶다면
# set.seed() 함수를 이용하여 seed값을 지정해주면 된다.
set.seed(100)
sample(1:20, size = 5)
## [1] 10 6 16 14 12
set.seed(100)
sample(1:20, size = 5)
## [1] 10 6 16 14 12
set.seed(100)
sample(1:20, size = 5)
## [1] 10 6 16 14 12combn(1:5, 3) # 1~5에서 3개를 뽑는 조합
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 1 1 1 1 1 1 2 2 2 3
## [2,] 2 2 2 3 3 4 3 3 4 4
## [3,] 3 4 5 4 5 5 4 5 5 5
x = c("red", "green", "blue", "black", "white")
com <- combn(x, 2) # x의 원소를 2개씩 뽑는 조합
com
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
## [1,] "red" "red" "red" "red" "green" "green" "green" "blue" "blue"
## [2,] "green" "blue" "black" "white" "blue" "black" "white" "black" "white"
## [,10]
## [1,] "black"
## [2,] "white"
for (i in 1:ncol(com)) {
# 조합을 출력
cat(com[, i], "\n")
}
## red green
## red blue
## red black
## red white
## green blue
## green black
## green white
## blue black
## blue white
## black white# aggregate(data, by = '기준이 되는 컬럼', FUN)
agg <- aggregate(iris[, -5], by = list(iris$Species), FUN = mean)
agg
## Group.1 Sepal.Length Sepal.Width Petal.Length Petal.Width
## 1 setosa 5.006 3.428 1.462 0.246
## 2 versicolor 5.936 2.770 4.260 1.326
## 3 virginica 6.588 2.974 5.552 2.026# aggregate는 데이터의 특정 컬럼을 기준으로 통계량을 구해주는 함수
agg <- aggregate(iris[, -5], by = list(표준편차 = iris$Species), FUN = sd)
agg
## 표준편차 Sepal.Length Sepal.Width Petal.Length Petal.Width
## 1 setosa 0.3524897 0.3790644 0.1736640 0.1053856
## 2 versicolor 0.5161711 0.3137983 0.4699110 0.1977527
## 3 virginica 0.6358796 0.3224966 0.5518947 0.2746501head(mtcars)
## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
agg <- aggregate(mtcars, by = list(cyl = mtcars$cyl, vs = mtcars$vs), FUN = max)
agg
## cyl vs mpg cyl disp hp drat wt qsec vs am gear carb
## 1 4 0 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## 2 6 0 21.0 6 160.0 175 3.90 2.875 17.02 0 1 5 6
## 3 8 0 19.2 8 472.0 335 4.22 5.424 18.00 0 1 5 8
## 4 4 1 33.9 4 146.7 113 4.93 3.190 22.90 1 1 5 2
## 5 6 1 21.4 6 258.0 123 3.92 3.460 20.22 1 0 4 4
agg <- aggregate(mtcars, by = list(cyl = mtcars$cyl, vs = mtcars$vs), FUN = mean)
agg
## cyl vs mpg cyl disp hp drat wt qsec vs am
## 1 4 0 26.00000 4 120.30 91.0000 4.430000 2.140000 16.70000 0 1.0000000
## 2 6 0 20.56667 6 155.00 131.6667 3.806667 2.755000 16.32667 0 1.0000000
## 3 8 0 15.10000 8 353.10 209.2143 3.229286 3.999214 16.77214 0 0.1428571
## 4 4 1 26.73000 4 103.62 81.8000 4.035000 2.300300 19.38100 1 0.7000000
## 5 6 1 19.12500 6 204.55 115.2500 3.420000 3.388750 19.21500 1 0.0000000
## gear carb
## 1 5.000000 2.000000
## 2 4.333333 4.666667
## 3 3.285714 3.500000
## 4 4.000000 1.500000
## 5 3.500000 2.500000x <- data.frame(name = c("a", "b", "c"), math = c(90, 80, 40))
y <- data.frame(name = c("a", "b", "d"), korean = c(75, 60, 90))
x ; y
## name math
## 1 a 90
## 2 b 80
## 3 c 40
## name korean
## 1 a 75
## 2 b 60
## 3 d 90z <- merge(x, y, by = c("name"))
z
## name math korean
## 1 a 90 75
## 2 b 80 60merge(x, y, all.x = T) # 첫 번째 데이터셋의 행들은 모두 표시되도록
## name math korean
## 1 a 90 75
## 2 b 80 60
## 3 c 40 NA
merge(x, y, all.y = T) # 두 번째 데이터셋의 행들은 모두 표시되도록
## name math korean
## 1 a 90 75
## 2 b 80 60
## 3 d NA 90
merge(x, y, all = T) # 두 데이터셋의 모든 행들이 표시되도록
## name math korean
## 1 a 90 75
## 2 b 80 60
## 3 c 40 NA
## 4 d NA 90x <- data.frame(name = c("a", "b", "c"), math = c(90, 80, 40))
y <- data.frame(sname = c("a", "b", "d"), korean = c(75, 60, 90))
x # 병합 기준 열의 이름이 name
## name math
## 1 a 90
## 2 b 80
## 3 c 40
y # 병합 기준 열의 이름이 sname
## sname korean
## 1 a 75
## 2 b 60
## 3 d 90
merge(x, y, by.x = c("name"), by.y = c("sname"))
## name math korean
## 1 a 90 75
## 2 b 80 60